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Some years ago I was on a panel that was interviewing candidates for a relatively senior embedded C programmer position.
One of the standard questions that I asked was about optimisation techniques. I was quite surprised that some of the candidates didn't have answers.
So, in the interests of putting together a list for posterity - what techniques and constructs do you normally use when optimising C programs?
Answers to optimisation for speed and size both accepted.
First things first - don't optimise too early. It's not uncommon to spend time carefully optimising a chunk of code only to find that it wasn't the bottleneck that you thought it was going to be. Or, to put it another way "Before you make it fast, make it work"
Investigate whether there's any option for optimising the algorithm before optimising the code. It'll be easier to find an improvement in performance by optimising a poor algorithm than it is to optimise the code, only then to throw it away when you change the algorithm anyway.
And work out why you need to optimise in the first place. What are you trying to achieve? If you're trying, say, to improve the response time to some event work out if there is an opportunity to change the order of execution to minimise the time critical areas. For example when trying to improve the response to some external interrupt can you do any preparation in the dead time between events?
Once you've decided that you need to optimise the code, which bit do you optimise? Use a profiler. Focus your attention (first) on the areas that are used most often.
So what can you do about those areas?
minimise condition checking. Checking conditions (eg. terminating conditions for loops) is time that isn't being spent on actual processing. Condition checking can be minimised with techniques like loop-unrolling.
In some circumstances condition checking can also be eliminated by using function pointers. For example if you are implementing a state machine you may find that implementing the handlers for individual states as small functions (with a uniform prototype) and storing the "next state" by storing the function pointer of the next handler is more efficient than using a large switch statement with the handler code implemented in the individual case statements. YMMV.
minimise function calls. Function calls usually carry a burden of context saving (eg. writing local variables contained in registers to the stack, saving the stack pointer), so if you don't have to make a call this is time saved. One option (if you're optimising for speed and not space) is to make use of inline functions.
If function calls are unavoidable minimise the data that is being passed to the functions. For example passing pointers is likely to be more efficient than passing structures.
When optimising for speed choose datatypes that are the native size for your platform. For example on a 32bit processor it is likely to be more efficient to manipulate 32bit values than 8 or 16 bit values. (side note - it is worth checking that the compiler is doing what you think it is. I've had situations where I've discovered that my compiler insisted on doing 16 bit arithmetic on 8 bit values with all of the to and from conversions to go with them)
Find data that can be precalculated, and either calculate during initialisation or (better yet) at compile time. For example when implementing a CRC you can either calculate your CRC values on the fly (using the polynomial directly) which is great for size (but dreadful for performance), or you can generate a table of all of the interim values - which is a much faster implementation, to the detriment of the size.
Localise your data. If you're manipulating a blob of data often your processor may be able to speed things up by storing it all in cache. And your compiler may be able to use shorter instructions that are suited to more localised data (eg. instructions that use 8 bit offsets instead of 32 bit)
In the same vein, localise your functions. For the same reasons.
Work out the assumptions that you can make about the operations that you're performing and find ways of exploiting them. For example, on an 8 bit platform if the only operation that at you're doing on a 32 bit value is an increment you may find that you can do better than the compiler by inlining (or creating a macro) specifically for this purpose, rather than using a normal arithmetic operation.
Avoid expensive instructions - division is a prime example.
The "register" keyword can be your friend (although hopefully your compiler has a pretty good idea about your register usage). If you're going to use "register" it's likely that you'll have to declare the local variables that you want "register"ed first.
Be consistent with your data types. If you are doing arithmetic on a mixture of data types (eg. shorts and ints, doubles and floats) then the compiler is adding implicit type conversions for each mismatch. This is wasted cpu cycles that may not be necessary.
Most of the options listed above can be used as part of normal practice without any ill effects. However if you're really trying to eke out the best performance:
- Investigate where you can (safely) disable error checking. It's not recommended, but it will save you some space and cycles.
- Hand craft portions of your code in assembler. This of course means that your code is no longer portable but where that's not an issue you may find savings here. Be aware though that there is potentially time lost moving data into and out of the registers that you have at your disposal (ie. to satisfy the register usage of your compiler). Also be aware that your compiler should be doing a pretty good job on its own. (of course there are exceptions)
As everybody else has said: profile, profile profile.
As for actual techniques, one that I don't think has been mentioned yet:
Hot & Cold Data Separation: Staying within the CPU's cache is incredibly important. One way of helping to do this is by splitting your data structures into frequently accessed ("hot") and rarely accessed ("cold") sections.
An example: Suppose you have a structure for a customer that looks something like this:
struct Customer
{
int ID;
int AccountNumber;
char Name[128];
char Address[256];
};
Customer customers[1000];
Now, lets assume that you want to access the ID and AccountNumber a lot, but not so much the name and address. What you'd do is to split it into two:
struct CustomerAccount
{
int ID;
int AccountNumber;
CustomerData *pData;
};
struct CustomerData
{
char Name[128];
char Address[256];
};
CustomerAccount customers[1000];
In this way, when you're looping through your "customers" array, each entry is only 12 bytes and so you can fit many more entries in the cache. This can be a huge win if you can apply it to situations like the inner loop of a rendering engine.
My favorite technique is to use a good profiler. Without a good profile telling you where the bottleneck lies, no tricks and techniques are going to help you.
most common techniques I encountered are:
loop unrolling
loop optimization for better cache prefetch
(i.e. do N operations in M cycles instead of NxM singular operations)
data aligning
inline functions
hand-crafted asm snippets
As for general recommendations, most of them are already sounded:
choose better algos
use profiler
don't optimize if it doesn't give 20-30% performance boost
For low-level optimization:
START_TIMER/STOP_TIMER macros from ffmpeg (clock-level accuracy for measurement of any code).
Oprofile, of course, for profiling.
Enormous amounts of hand-coded assembly (just do a wc -l on x264's /common/x86 directory, and then remember most of the code is templated).
Careful coding in general; shorter code is usually better.
Smart low-level algorithms, like the 64-bit bitstream writer I wrote that uses only a single if and no else.
Explicit write-combining.
Taking into account important weird aspects of processors, like Intel's cacheline split issue.
Finding cases where one can losslessly or near-losslessly make an early termination, where the early-termination check costs much less than the speed one gains from it.
Actually inlined assembly for tasks which are far more suited to the x86 SIMD unit, such as median calculations (requires compile-time check for MMX support).
First and foremost, use a better/faster algorithm. There is no point optimizing code that is slow by design.
When optimizing for speed, trade memory for speed: lookup tables of precomputed values, binary trees, write faster custom implementation of system calls...
When trading speed for memory: use in-memory compression
Avoid using the heap. Use obstacks or pool-allocator for identical sized objects. Put small things with short lifetime onto the stack. alloca still exists.
Pre-mature optimization is the root of all evil!
;)
As my applications usually don't need much CPU time by design, I focus on the size my binaries on disk and in memory. What I do mostly is looking out for statically sized arrays and replacing them with dynamically allocated memory where it's worth the additional effort of free'ing the memory later. To cut down the size of the binary, I look for big arrays that are initialized at compile time and put the initializiation to runtime.
char buf[1024] = { 0, };
/* becomes: */
char buf[1024];
memset(buf, 0, sizeof(buf));
This will remove the 1024 zero-bytes from the binaries .DATA section and will instead create the buffer on the stack at runtime and the fill it with zeros.
EDIT: Oh yeah, and I like to cache things. It's not C specific but depending on what you're caching, it can give you a huge boost in performance.
PS: Please let us know when your list is finished, I'm very curious. ;)
If possible, compare with 0, not with arbitrary numbers, especially in loops, because comparison with 0 is often implemented with separate, faster assembler commands.
For example, if possible, write
for (i=n; i!=0; --i) { ... }
instead of
for (i=0; i!=n; ++i) { ... }
Another thing that was not mentioned:
Know your requirements: don't optimize for situations that will unlikely or never happen, concentrate on the most bang for the buck
basics/general:
Do not optimize when you have no problem.
Know your platform/CPU...
...know it thoroughly
know your ABI
Let the compiler do the optimization, just help it with the job.
some things that have actually helped:
Opt for size/memory:
Use bitfields for storing bools
re-use big global arrays by overlaying with a union (be careful)
Opt for speed (be careful):
use precomputed tables where possible
place critical functions/data in fast memory
Use dedicated registers for often used globals
count to-zero, zero flag is free
Difficult to summarize ...
Data structures:
Splitting of a data structure depending on case of usage is extremely important. It is common to see a structure that holds data that is accessed based on a flow control. This situation can lower significantly the cache usage.
To take into account cache line size and prefetch rules.
To reorder the members of the structure to obtain a sequential access to them from your code
Algorithms:
Take time to think about your problem and to find the correct algorithm.
Know the limitations of the algorithm you choose (a radix-sort/quick-sort for 10 elements to be sorted might not be the best choice).
Low level:
As for the latest processors it is not recommended to unroll a loop that has a small body. The processor provides its own detection mechanism for this and will short-circuit whole section of its pipeline.
Trust the HW prefetcher. Of course if your data structures are well designed ;)
Care about your L2 cache line misses.
Try to reduce as much as possible the local working set of your application as the processors are leaning to smaller caches per cores (C2D enjoyed a 3MB per core max where iCore7 will provide a max of 256KB per core + 8MB shared to all cores for a quad core die.).
The most important of all: Measure early, Measure often and never ever makes assumptions, base your thinking and optimizations on data retrieved by a profiler (please use PTU).
Another hint, performance is key to the success of an application and should be considered at design time and you should have clear performance targets.
This is far from being exhaustive but should provide an interesting base.
These days, the most important things in optimzation are:
respecting the cache - try to access memory in simple patterns, and don't unroll loops just for fun. Use arrays instead of data structures with lots of pointer chasing and it'll probably be faster for small amounts of data. And don't make anything too big.
avoiding latency - try to avoid divisions and stuff that's slow if other calculations depend on them immediately. Memory accesses that depend on other memory accesses (ie, a[b[c]]) are bad.
avoiding unpredictabilty - a lot of if/elses with unpredictable conditions, or conditions that introduce more latency, will really mess you up. There's a lot of branchless math tricks that are useful here, but they increase latency and are only useful if you really need them. Otherwise, just write simple code and don't have crazy loop conditions.
Don't bother with optimizations that involve copy-and-pasting your code (like loop unrolling), or reordering loops by hand. The compiler usually does a better job than you at doing this, but most of them aren't smart enough to undo it.
Collecting profiles of code execution get you 50% of the way there. The other 50% deals with analyzing these reports.
Further, if you use GCC or VisualC++, you can use "profile guided optimization" where the compiler will take info from previous executions and reschedule instructions to make the CPU happier.
Inline functions! Inspired by the profiling fans here I profiled an application of mine and found a small function that does some bitshifting on MP3 frames. It makes about 90% of all function calls in my applcation, so I made it inline and voila - the program now uses half of the CPU time it did before.
On most of embedded system i worked there was no profiling tools, so it's nice to say use profiler but not very practical.
First rule in speed optimization is - find your critical path.
Usually you will find that this path is not so long and not so complex. It's hard to say in generic way how to optimize this it's depend on what are you doing and what is in your power to do. For example you want usually avoid memcpy on critical path, so ever you need to use DMA or optimize, but what if you hw does not have DMA ? check if memcpy implementation is a best one if not rewrite it.
Do not use dynamic allocation at all in embedded but if you do for some reason don't do it in critical path.
Organize your thread priorities correctly, what is correctly is real question and it's clearly system specific.
We use very simple tools to analyze the bottle-necks, simple macro that store the time-stamp and index. Few (2-3) runs in 90% of cases will find where you spend your time.
And the last one is code review a very important one. In most case we avoid performance problem during code review very effective way :)
Measure performance.
Use realistic and non-trivial benchmarks. Remember that "everything is fast for small N".
Use a profiler to find hotspots.
Reduce number of dynamic memory allocations, disk accesses, database accesses, network accesses, and user/kernel transitions, because these often tend to be hotspots.
Measure performance.
In addition, you should measure performance.
Sometimes you have to decide whether it is more space or more speed that you are after, which will lead to almost opposite optimizations. For example, to get the most out of you space, you pack structures e.g. #pragma pack(1) and use bit fields in structures. For more speed you pack to align with the processors preference and avoid bitfields.
Another trick is picking the right re-sizing algorithms for growing arrays via realloc, or better still writing your own heap manager based on your particular application. Don't assume the one that comes with the compiler is the best possible solution for every application.
If someone doesn't have an answer to that question, it could be they don't know much.
It could also be that they know a lot. I know a lot (IMHO :-), and if I were asked that question, I would be asking you back: Why do you think that's important?
The problem is, any a-priori notions about performance, if they are not informed by a specific situation, are guesses by definition.
I think it is important to know coding techniques for performance, but I think it is even more important to know not to use them, until diagnosis reveals that there is a problem and what it is.
Now I'm going to contradict myself and say, if you do that, you learn how to recognize the design approaches that lead to trouble so you can avoid them, and to a novice, that sounds like premature optimization.
To give you a concrete example, this is a C application that was optimized.
Great lists. I will just add one tip I didn't saw in the above lists that in some case can yield huge optimisation for minimal cost.
bypass linker
if you have some application divided in two files, say main.c and lib.c, in many cases you can just add a \#include "lib.c" in your main.c That will completely bypass linker and allow for much more efficient optimisation for compiler.
The same effect can be achieved optimizing dependencies between files, but the cost of changes is usually higher.
Sometimes Google is the best algorithm optimization tool. When I have a complex problem, a bit of searching reveals some guys with PhD's have found a mapping between this and a well-known problem and have already done most of the work.
I would recommend optimizing using more efficient algorithms and not do it as an afterthought but code it that way from the start. Let the compiler work out the details on the small things as it knows more about the target processor than you do.
For one, I rarely use loops to look things up, I add items to a hashtable and then use the hashtable to lookup the results.
For example you have a string to lookup and then 50 possible values. So instead of doing 50 strcmps, you add all 50 strings to a hashtable and give each a unique number ( you only have to do this once ). Then you lookup the target string in the hashtable and have one large switch with all 50 cases ( or have functions pointers ).
When looking up things with common sets of input ( like css rules ), I use fast code to keep track of the only possible solitions and then iterate thought those to find a match. Once I have a match I save the results into a hashtable ( as a cache ) and then use the cache results if I get that same input set later.
My main tools for faster code are:
hashtable - for quick lookups and for caching results
qsort - it's the only sort I use
bsp - for looking up things based on area ( map rendering etc )
We are compiling an embedded C++ application that is deployed in a shielded device in an environment bombarded with ionizing radiation. We are using GCC and cross-compiling for ARM. When deployed, our application generates some erroneous data and crashes more often than we would like. The hardware is designed for this environment, and our application has run on this platform for several years.
Are there changes we can make to our code, or compile-time improvements that can be made to identify/correct soft errors and memory-corruption caused by single event upsets? Have any other developers had success in reducing the harmful effects of soft errors on a long-running application?
Working for about 4-5 years with software/firmware development and environment testing of miniaturized satellites*, I would like to share my experience here.
*(miniaturized satellites are a lot more prone to single event upsets than bigger satellites due to its relatively small, limited sizes for its electronic components)
To be very concise and direct: there is no mechanism to recover from detectable, erroneous
situation by the software/firmware itself without, at least, one
copy of minimum working version of the software/firmware somewhere for recovery purpose - and with the hardware supporting the recovery (functional).
Now, this situation is normally handled both in the hardware and software level. Here, as you request, I will share what we can do in the software level.
...recovery purpose.... Provide ability to update/recompile/reflash your software/firmware in real environment. This is an almost must-have feature for any software/firmware in highly ionized environment. Without this, you could have redundant software/hardware as many as you want but at one point, they are all going to blow up. So, prepare this feature!
...minimum working version... Have responsive, multiple copies, minimum version of the software/firmware in your code. This is like Safe mode in Windows. Instead of having only one, fully functional version of your software, have multiple copies of the minimum version of your software/firmware. The minimum copy will usually having much less size than the full copy and almost always have only the following two or three features:
capable of listening to command from external system,
capable of updating the current software/firmware,
capable of monitoring the basic operation's housekeeping data.
...copy... somewhere... Have redundant software/firmware somewhere.
You could, with or without redundant hardware, try to have redundant software/firmware in your ARM uC. This is normally done by having two or more identical software/firmware in separate addresses which sending heartbeat to each other - but only one will be active at a time. If one or more software/firmware is known to be unresponsive, switch to the other software/firmware. The benefit of using this approach is we can have functional replacement immediately after an error occurs - without any contact with whatever external system/party who is responsible to detect and to repair the error (in satellite case, it is usually the Mission Control Centre (MCC)).
Strictly speaking, without redundant hardware, the disadvantage of doing this is you actually cannot eliminate all single point of failures. At the very least, you will still have one single point of failure, which is the switch itself (or often the beginning of the code). Nevertheless, for a device limited by size in a highly ionized environment (such as pico/femto satellites), the reduction of the single point of failures to one point without additional hardware will still be worth considering. Somemore, the piece of code for the switching would certainly be much less than the code for the whole program - significantly reducing the risk of getting Single Event in it.
But if you are not doing this, you should have at least one copy in your external system which can come in contact with the device and update the software/firmware (in the satellite case, it is again the mission control centre).
You could also have the copy in your permanent memory storage in your device which can be triggered to restore the running system's software/firmware
...detectable erroneous situation.. The error must be detectable, usually by the hardware error correction/detection circuit or by a small piece of code for error correction/detection. It is best to put such code small, multiple, and independent from the main software/firmware. Its main task is only for checking/correcting. If the hardware circuit/firmware is reliable (such as it is more radiation hardened than the rests - or having multiple circuits/logics), then you might consider making error-correction with it. But if it is not, it is better to make it as error-detection. The correction can be by external system/device. For the error correction, you could consider making use of a basic error correction algorithm like Hamming/Golay23, because they can be implemented more easily both in the circuit/software. But it ultimately depends on your team's capability. For error detection, normally CRC is used.
...hardware supporting the recovery Now, comes to the most difficult aspect on this issue. Ultimately, the recovery requires the hardware which is responsible for the recovery to be at least functional. If the hardware is permanently broken (normally happen after its Total ionizing dose reaches certain level), then there is (sadly) no way for the software to help in recovery. Thus, hardware is rightly the utmost importance concern for a device exposed to high radiation level (such as satellite).
In addition to the suggestion for above anticipating firmware's error due to single event upset, I would also like to suggest you to have:
Error detection and/or error correction algorithm in the inter-subsystem communication protocol. This is another almost must have in order to avoid incomplete/wrong signals received from other system
Filter in your ADC reading. Do not use the ADC reading directly. Filter it by median filter, mean filter, or any other filters - never trust single reading value. Sample more, not less - reasonably.
NASA has a paper on radiation-hardened software. It describes three main tasks:
Regular monitoring of memory for errors then scrubbing out those errors,
robust error recovery mechanisms, and
the ability to reconfigure if something no longer works.
Note that the memory scan rate should be frequent enough that multi-bit errors rarely occur, as most ECC memory can recover from single-bit errors, not multi-bit errors.
Robust error recovery includes control flow transfer (typically restarting a process at a point before the error), resource release, and data restoration.
Their main recommendation for data restoration is to avoid the need for it, through having intermediate data be treated as temporary, so that restarting before the error also rolls back the data to a reliable state. This sounds similar to the concept of "transactions" in databases.
They discuss techniques particularly suitable for object-oriented languages such as C++. For example
Software-based ECCs for contiguous memory objects
Programming by Contract: verifying preconditions and postconditions, then checking the object to verify it is still in a valid state.
And, it just so happens, NASA has used C++ for major projects such as the Mars Rover.
C++ class abstraction and encapsulation enabled rapid development and testing among multiple projects and developers.
They avoided certain C++ features that could create problems:
Exceptions
Templates
Iostream (no console)
Multiple inheritance
Operator overloading (other than new and delete)
Dynamic allocation (used a dedicated memory pool and placement new to avoid the possibility of system heap corruption).
Here are some thoughts and ideas:
Use ROM more creatively.
Store anything you can in ROM. Instead of calculating things, store look-up tables in ROM. (Make sure your compiler is outputting your look-up tables to the read-only section! Print out memory addresses at runtime to check!) Store your interrupt vector table in ROM. Of course, run some tests to see how reliable your ROM is compared to your RAM.
Use your best RAM for the stack.
SEUs in the stack are probably the most likely source of crashes, because it is where things like index variables, status variables, return addresses, and pointers of various sorts typically live.
Implement timer-tick and watchdog timer routines.
You can run a "sanity check" routine every timer tick, as well as a watchdog routine to handle the system locking up. Your main code could also periodically increment a counter to indicate progress, and the sanity-check routine could ensure this has occurred.
Implement error-correcting-codes in software.
You can add redundancy to your data to be able to detect and/or correct errors. This will add processing time, potentially leaving the processor exposed to radiation for a longer time, thus increasing the chance of errors, so you must consider the trade-off.
Remember the caches.
Check the sizes of your CPU caches. Data that you have accessed or modified recently will probably be within a cache. I believe you can disable at least some of the caches (at a big performance cost); you should try this to see how susceptible the caches are to SEUs. If the caches are hardier than RAM then you could regularly read and re-write critical data to make sure it stays in cache and bring RAM back into line.
Use page-fault handlers cleverly.
If you mark a memory page as not-present, the CPU will issue a page fault when you try to access it. You can create a page-fault handler that does some checking before servicing the read request. (PC operating systems use this to transparently load pages that have been swapped to disk.)
Use assembly language for critical things (which could be everything).
With assembly language, you know what is in registers and what is in RAM; you know what special RAM tables the CPU is using, and you can design things in a roundabout way to keep your risk down.
Use objdump to actually look at the generated assembly language, and work out how much code each of your routines takes up.
If you are using a big OS like Linux then you are asking for trouble; there is just so much complexity and so many things to go wrong.
Remember it is a game of probabilities.
A commenter said
Every routine you write to catch errors will be subject to failing itself from the same cause.
While this is true, the chances of errors in the (say) 100 bytes of code and data required for a check routine to function correctly is much smaller than the chance of errors elsewhere. If your ROM is pretty reliable and almost all the code/data is actually in ROM then your odds are even better.
Use redundant hardware.
Use 2 or more identical hardware setups with identical code. If the results differ, a reset should be triggered. With 3 or more devices you can use a "voting" system to try to identify which one has been compromised.
You may also be interested in the rich literature on the subject of algorithmic fault tolerance. This includes the old assignment: Write a sort that correctly sorts its input when a constant number of comparisons will fail (or, the slightly more evil version, when the asymptotic number of failed comparisons scales as log(n) for n comparisons).
A place to start reading is Huang and Abraham's 1984 paper "Algorithm-Based Fault Tolerance for Matrix Operations". Their idea is vaguely similar to homomorphic encrypted computation (but it is not really the same, since they are attempting error detection/correction at the operation level).
A more recent descendant of that paper is Bosilca, Delmas, Dongarra, and Langou's "Algorithm-based fault tolerance applied to high performance computing".
Writing code for radioactive environments is not really any different than writing code for any mission-critical application.
In addition to what has already been mentioned, here are some miscellaneous tips:
Use everyday "bread & butter" safety measures that should be present on any semi-professional embedded system: internal watchdog, internal low-voltage detect, internal clock monitor. These things shouldn't even need to be mentioned in the year 2016 and they are standard on pretty much every modern microcontroller.
If you have a safety and/or automotive-oriented MCU, it will have certain watchdog features, such as a given time window, inside which you need to refresh the watchdog. This is preferred if you have a mission-critical real-time system.
In general, use a MCU suitable for these kind of systems, and not some generic mainstream fluff you received in a packet of corn flakes. Almost every MCU manufacturer nowadays have specialized MCUs designed for safety applications (TI, Freescale, Renesas, ST, Infineon etc etc). These have lots of built-in safety features, including lock-step cores: meaning that there are 2 CPU cores executing the same code, and they must agree with each other.
IMPORTANT: You must ensure the integrity of internal MCU registers. All control & status registers of hardware peripherals that are writeable may be located in RAM memory, and are therefore vulnerable.
To protect yourself against register corruptions, preferably pick a microcontroller with built-in "write-once" features of registers. In addition, you need to store default values of all hardware registers in NVM and copy-down those values to your registers at regular intervals. You can ensure the integrity of important variables in the same manner.
Note: always use defensive programming. Meaning that you have to setup all registers in the MCU and not just the ones used by the application. You don't want some random hardware peripheral to suddenly wake up.
There are all kinds of methods to check for errors in RAM or NVM: checksums, "walking patterns", software ECC etc etc. The best solution nowadays is to not use any of these, but to use a MCU with built-in ECC and similar checks. Because doing this in software is complex, and the error check in itself could therefore introduce bugs and unexpected problems.
Use redundancy. You could store both volatile and non-volatile memory in two identical "mirror" segments, that must always be equivalent. Each segment could have a CRC checksum attached.
Avoid using external memories outside the MCU.
Implement a default interrupt service routine / default exception handler for all possible interrupts/exceptions. Even the ones you are not using. The default routine should do nothing except shutting off its own interrupt source.
Understand and embrace the concept of defensive programming. This means that your program needs to handle all possible cases, even those that cannot occur in theory. Examples.
High quality mission-critical firmware detects as many errors as possible, and then handles or ignores them in a safe manner.
Never write programs that rely on poorly-specified behavior. It is likely that such behavior might change drastically with unexpected hardware changes caused by radiation or EMI. The best way to ensure that your program is free from such crap is to use a coding standard like MISRA, together with a static analyser tool. This will also help with defensive programming and with weeding out bugs (why would you not want to detect bugs in any kind of application?).
IMPORTANT: Don't implement any reliance of the default values of static storage duration variables. That is, don't trust the default contents of the .data or .bss. There could be any amount of time between the point of initialization to the point where the variable is actually used, there could have been plenty of time for the RAM to get corrupted. Instead, write the program so that all such variables are set from NVM in run-time, just before the time when such a variable is used for the first time.
In practice this means that if a variable is declared at file scope or as static, you should never use = to initialize it (or you could, but it is pointless, because you cannot rely on the value anyhow). Always set it in run-time, just before use. If it is possible to repeatedly update such variables from NVM, then do so.
Similarly in C++, don't rely on constructors for static storage duration variables. Have the constructor(s) call a public "set-up" routine, which you can also call later on in run-time, straight from the caller application.
If possible, remove the "copy-down" start-up code that initializes .data and .bss (and calls C++ constructors) entirely, so that you get linker errors if you write code relying on such. Many compilers have the option to skip this, usually called "minimal/fast start-up" or similar.
This means that any external libraries have to be checked so that they don't contain any such reliance.
Implement and define a safe state for the program, to where you will revert in case of critical errors.
Implementing an error report/error log system is always helpful.
It may be possible to use C to write programs that behave robustly in such environments, but only if most forms of compiler optimization are disabled. Optimizing compilers are designed to replace many seemingly-redundant coding patterns with "more efficient" ones, and may have no clue that the reason the programmer is testing x==42 when the compiler knows there's no way x could possibly hold anything else is because the programmer wants to prevent the execution of certain code with x holding some other value--even in cases where the only way it could hold that value would be if the system received some kind of electrical glitch.
Declaring variables as volatile is often helpful, but may not be a panacea.
Of particular importance, note that safe coding often requires that dangerous
operations have hardware interlocks that require multiple steps to activate,
and that code be written using the pattern:
... code that checks system state
if (system_state_favors_activation)
{
prepare_for_activation();
... code that checks system state again
if (system_state_is_valid)
{
if (system_state_favors_activation)
trigger_activation();
}
else
perform_safety_shutdown_and_restart();
}
cancel_preparations();
If a compiler translates the code in relatively literal fashion, and if all
the checks for system state are repeated after the prepare_for_activation(),
the system may be robust against almost any plausible single glitch event,
even those which would arbitrarily corrupt the program counter and stack. If
a glitch occurs just after a call to prepare_for_activation(), that would imply
that activation would have been appropriate (since there's no other reason
prepare_for_activation() would have been called before the glitch). If the
glitch causes code to reach prepare_for_activation() inappropriately, but there
are no subsequent glitch events, there would be no way for code to subsequently
reach trigger_activation() without having passed through the validation check or calling cancel_preparations first [if the stack glitches, execution might proceed to a spot just before trigger_activation() after the context that called prepare_for_activation() returns, but the call to cancel_preparations() would have occurred between the calls to prepare_for_activation() and trigger_activation(), thus rendering the latter call harmless.
Such code may be safe in traditional C, but not with modern C compilers. Such compilers can be very dangerous in that sort of environment because aggressive they strive to only include code which will be relevant in situations that could come about via some well-defined mechanism and whose resulting consequences would also be well defined. Code whose purpose would be to detect and clean up after failures may, in some cases, end up making things worse. If the compiler determines that the attempted recovery would in some cases invoke undefined behavior, it may infer that the conditions that would necessitate such recovery in such cases cannot possibly occur, thus eliminating the code that would have checked for them.
This is an extremely broad subject. Basically, you can't really recover from memory corruption, but you can at least try to fail promptly. Here are a few techniques you could use:
checksum constant data. If you have any configuration data which stays constant for a long time (including hardware registers you have configured), compute its checksum on initialization and verify it periodically. When you see a mismatch, it's time to re-initialize or reset.
store variables with redundancy. If you have an important variable x, write its value in x1, x2 and x3 and read it as (x1 == x2) ? x2 : x3.
implement program flow monitoring. XOR a global flag with a unique value in important functions/branches called from the main loop. Running the program in a radiation-free environment with near-100% test coverage should give you the list of acceptable values of the flag at the end of the cycle. Reset if you see deviations.
monitor the stack pointer. In the beginning of the main loop, compare the stack pointer with its expected value. Reset on deviation.
What could help you is a watchdog. Watchdogs were used extensively in industrial computing in the 1980s. Hardware failures were much more common then - another answer also refers to that period.
A watchdog is a combined hardware/software feature. The hardware is a simple counter that counts down from a number (say 1023) to zero. TTL or other logic could be used.
The software has been designed as such that one routine monitors the correct operation of all essential systems. If this routine completes correctly = finds the computer running fine, it sets the counter back to 1023.
The overall design is so that under normal circumstances, the software prevents that the hardware counter will reach zero. In case the counter reaches zero, the hardware of the counter performs its one-and-only task and resets the entire system. From a counter perspective, zero equals 1024 and the counter continues counting down again.
This watchdog ensures that the attached computer is restarted in a many, many cases of failure. I must admit that I'm not familiar with hardware that is able to perform such a function on today's computers. Interfaces to external hardware are now a lot more complex than they used to be.
An inherent disadvantage of the watchdog is that the system is not available from the time it fails until the watchdog counter reaches zero + reboot time. While that time is generally much shorter than any external or human intervention, the supported equipment will need to be able to proceed without computer control for that timeframe.
This answer assumes you are concerned with having a system that works correctly, over and above having a system that is minimum cost or fast; most people playing with radioactive things value correctness / safety over speed / cost
Several people have suggested hardware changes you can make (fine - there's lots of good stuff here in answers already and I don't intend repeating all of it), and others have suggested redundancy (great in principle), but I don't think anyone has suggested how that redundancy might work in practice. How do you fail over? How do you know when something has 'gone wrong'? Many technologies work on the basis everything will work, and failure is thus a tricky thing to deal with. However, some distributed computing technologies designed for scale expect failure (after all with enough scale, failure of one node of many is inevitable with any MTBF for a single node); you can harness this for your environment.
Here are some ideas:
Ensure that your entire hardware is replicated n times (where n is greater than 2, and preferably odd), and that each hardware element can communicate with each other hardware element. Ethernet is one obvious way to do that, but there are many other far simpler routes that would give better protection (e.g. CAN). Minimise common components (even power supplies). This may mean sampling ADC inputs in multiple places for instance.
Ensure your application state is in a single place, e.g. in a finite state machine. This can be entirely RAM based, though does not preclude stable storage. It will thus be stored in several place.
Adopt a quorum protocol for changes of state. See RAFT for example. As you are working in C++, there are well known libraries for this. Changes to the FSM would only get made when a majority of nodes agree. Use a known good library for the protocol stack and the quorum protocol rather than rolling one yourself, or all your good work on redundancy will be wasted when the quorum protocol hangs up.
Ensure you checksum (e.g. CRC/SHA) your FSM, and store the CRC/SHA in the FSM itself (as well as transmitting in the message, and checksumming the messages themselves). Get the nodes to check their FSM regularly against these checksum, checksum incoming messages, and check their checksum matches the checksum of the quorum.
Build as many other internal checks into your system as possible, making nodes that detect their own failure reboot (this is better than carrying on half working provided you have enough nodes). Attempt to let them cleanly remove themselves from the quorum during rebooting in case they don't come up again. On reboot have them checksum the software image (and anything else they load) and do a full RAM test before reintroducing themselves to the quorum.
Use hardware to support you, but do so carefully. You can get ECC RAM, for instance, and regularly read/write through it to correct ECC errors (and panic if the error is uncorrectable). However (from memory) static RAM is far more tolerant of ionizing radiation than DRAM is in the first place, so it may be better to use static DRAM instead. See the first point under 'things I would not do' as well.
Let's say you have an 1% chance of failure of any given node within one day, and let's pretend you can make failures entirely independent. With 5 nodes, you'll need three to fail within one day, which is a .00001% chance. With more, well, you get the idea.
Things I would not do:
Underestimate the value of not having the problem to start off with. Unless weight is a concern, a large block of metal around your device is going to be a far cheaper and more reliable solution than a team of programmers can come up with. Ditto optical coupling of inputs of EMI is an issue, etc. Whatever, attempt when sourcing your components to source those rated best against ionizing radiation.
Roll your own algorithms. People have done this stuff before. Use their work. Fault tolerance and distributed algorithms are hard. Use other people's work where possible.
Use complicated compiler settings in the naive hope you detect more failures. If you are lucky, you may detect more failures. More likely, you will use a code-path within the compiler which has been less tested, particularly if you rolled it yourself.
Use techniques which are untested in your environment. Most people writing high availability software have to simulate failure modes to check their HA works correctly, and miss many failure modes as a result. You are in the 'fortunate' position of having frequent failures on demand. So test each technique, and ensure its application actual improves MTBF by an amount that exceeds the complexity to introduce it (with complexity comes bugs). Especially apply this to my advice re quorum algorithms etc.
Since you specifically ask for software solutions, and you are using C++, why not use operator overloading to make your own, safe datatypes? For example:
Instead of using uint32_t (and double, int64_t etc), make your own SAFE_uint32_t which contains a multiple (minimum of 3) of uint32_t. Overload all of the operations you want (* + - / << >> = == != etc) to perform, and make the overloaded operations perform independently on each internal value, ie don't do it once and copy the result. Both before and after, check that all of the internal values match. If values don't match, you can update the wrong one to the value with the most common one. If there is no most-common value, you can safely notify that there is an error.
This way it doesn't matter if corruption occurs in the ALU, registers, RAM, or on a bus, you will still have multiple attempts and a very good chance of catching errors. Note however though that this only works for the variables you can replace - your stack pointer for example will still be susceptible.
A side story: I ran into a similar issue, also on an old ARM chip. It turned out to be a toolchain which used an old version of GCC that, together with the specific chip we used, triggered a bug in certain edge cases that would (sometimes) corrupt values being passed into functions. Make sure your device doesn't have any problems before blaming it on radio-activity, and yes, sometimes it is a compiler bug =)
Disclaimer: I'm not a radioactivity professional nor worked for this kind of application. But I worked on soft errors and redundancy for long term archival of critical data, which is somewhat linked (same problem, different goals).
The main problem with radioactivity in my opinion is that radioactivity can switch bits, thus radioactivity can/will tamper any digital memory. These errors are usually called soft errors, bit rot, etc.
The question is then: how to compute reliably when your memory is unreliable?
To significantly reduce the rate of soft errors (at the expense of computational overhead since it will mostly be software-based solutions), you can either:
rely on the good old redundancy scheme, and more specifically the more efficient error correcting codes (same purpose, but cleverer algorithms so that you can recover more bits with less redundancy). This is sometimes (wrongly) also called checksumming. With this kind of solution, you will have to store the full state of your program at any moment in a master variable/class (or a struct?), compute an ECC, and check that the ECC is correct before doing anything, and if not, repair the fields. This solution however does not guarantee that your software can work (simply that it will work correctly when it can, or stops working if not, because ECC can tell you if something is wrong, and in this case you can stop your software so that you don't get fake results).
or you can use resilient algorithmic data structures, which guarantee, up to a some bound, that your program will still give correct results even in the presence of soft errors. These algorithms can be seen as a mix of common algorithmic structures with ECC schemes natively mixed in, but this is much more resilient than that, because the resiliency scheme is tightly bounded to the structure, so that you don't need to encode additional procedures to check the ECC, and usually they are a lot faster. These structures provide a way to ensure that your program will work under any condition, up to the theoretical bound of soft errors. You can also mix these resilient structures with the redundancy/ECC scheme for additional security (or encode your most important data structures as resilient, and the rest, the expendable data that you can recompute from the main data structures, as normal data structures with a bit of ECC or a parity check which is very fast to compute).
If you are interested in resilient data structures (which is a recent, but exciting, new field in algorithmics and redundancy engineering), I advise you to read the following documents:
Resilient algorithms data structures intro by Giuseppe F.Italiano, Universita di Roma "Tor Vergata"
Christiano, P., Demaine, E. D., & Kishore, S. (2011). Lossless fault-tolerant data structures with additive overhead. In Algorithms and Data Structures (pp. 243-254). Springer Berlin Heidelberg.
Ferraro-Petrillo, U., Grandoni, F., & Italiano, G. F. (2013). Data structures resilient to memory faults: an experimental study of dictionaries. Journal of Experimental Algorithmics (JEA), 18, 1-6.
Italiano, G. F. (2010). Resilient algorithms and data structures. In Algorithms and Complexity (pp. 13-24). Springer Berlin Heidelberg.
If you are interested in knowing more about the field of resilient data structures, you can checkout the works of Giuseppe F. Italiano (and work your way through the refs) and the Faulty-RAM model (introduced in Finocchi et al. 2005; Finocchi and Italiano 2008).
/EDIT: I illustrated the prevention/recovery from soft-errors mainly for RAM memory and data storage, but I didn't talk about computation (CPU) errors. Other answers already pointed at using atomic transactions like in databases, so I will propose another, simpler scheme: redundancy and majority vote.
The idea is that you simply do x times the same computation for each computation you need to do, and store the result in x different variables (with x >= 3). You can then compare your x variables:
if they all agree, then there's no computation error at all.
if they disagree, then you can use a majority vote to get the correct value, and since this means the computation was partially corrupted, you can also trigger a system/program state scan to check that the rest is ok.
if the majority vote cannot determine a winner (all x values are different), then it's a perfect signal for you to trigger the failsafe procedure (reboot, raise an alert to user, etc.).
This redundancy scheme is very fast compared to ECC (practically O(1)) and it provides you with a clear signal when you need to failsafe. The majority vote is also (almost) guaranteed to never produce corrupted output and also to recover from minor computation errors, because the probability that x computations give the same output is infinitesimal (because there is a huge amount of possible outputs, it's almost impossible to randomly get 3 times the same, even less chances if x > 3).
So with majority vote you are safe from corrupted output, and with redundancy x == 3, you can recover 1 error (with x == 4 it will be 2 errors recoverable, etc. -- the exact equation is nb_error_recoverable == (x-2) where x is the number of calculation repetitions because you need at least 2 agreeing calculations to recover using the majority vote).
The drawback is that you need to compute x times instead of once, so you have an additional computation cost, but's linear complexity so asymptotically you don't lose much for the benefits you gain. A fast way to do a majority vote is to compute the mode on an array, but you can also use a median filter.
Also, if you want to make extra sure the calculations are conducted correctly, if you can make your own hardware you can construct your device with x CPUs, and wire the system so that calculations are automatically duplicated across the x CPUs with a majority vote done mechanically at the end (using AND/OR gates for example). This is often implemented in airplanes and mission-critical devices (see triple modular redundancy). This way, you would not have any computational overhead (since the additional calculations will be done in parallel), and you have another layer of protection from soft errors (since the calculation duplication and majority vote will be managed directly by the hardware and not by software -- which can more easily get corrupted since a program is simply bits stored in memory...).
One point no-one seems to have mentioned. You say you're developing in GCC and cross-compiling onto ARM. How do you know that you don't have code which makes assumptions about free RAM, integer size, pointer size, how long it takes to do a certain operation, how long the system will run for continuously, or various stuff like that? This is a very common problem.
The answer is usually automated unit testing. Write test harnesses which exercise the code on the development system, then run the same test harnesses on the target system. Look for differences!
Also check for errata on your embedded device. You may find there's something about "don't do this because it'll crash, so enable that compiler option and the compiler will work around it".
In short, your most likely source of crashes is bugs in your code. Until you've made pretty damn sure this isn't the case, don't worry (yet) about more esoteric failure modes.
You want 3+ slave machines with a master outside the radiation environment. All I/O passes through the master which contains a vote and/or retry mechanism. The slaves must have a hardware watchdog each and the call to bump them should be surrounded by CRCs or the like to reduce the probability of involuntary bumping. Bumping should be controlled by the master, so lost connection with master equals reboot within a few seconds.
One advantage of this solution is that you can use the same API to the master as to the slaves, so redundancy becomes a transparent feature.
Edit: From the comments I feel the need to clarify the "CRC idea." The possibilty of the slave bumping it's own watchdog is close to zero if you surround the bump with CRC or digest checks on random data from the master. That random data is only sent from master when the slave under scrutiny is aligned with the others. The random data and CRC/digest are immediately cleared after each bump. The master-slave bump frequency should be more than double the watchdog timeout. The data sent from the master is uniquely generated every time.
How about running many instances of your application. If crashes are due to random memory bit changes, chances are some of your app instances will make it through and produce accurate results. It's probably quite easy (for someone with statistical background) to calculate how many instances do you need given bit flop probability to achieve as tiny overall error as you wish.
What you ask is quite complex topic - not easily answerable. Other answers are ok, but they covered just a small part of all the things you need to do.
As seen in comments, it is not possible to fix hardware problems 100%, however it is possible with high probabily to reduce or catch them using various techniques.
If I was you, I would create the software of the highest Safety integrity level level (SIL-4). Get the IEC 61513 document (for the nuclear industry) and follow it.
Someone mentioned using slower chips to prevent ions from flipping bits as easily. In a similar fashion perhaps use a specialized cpu/ram that actually uses multiple bits to store a single bit. Thus providing a hardware fault tolerance because it would be very unlikely that all of the bits would get flipped. So 1 = 1111 but would need to get hit 4 times to actually flipped. (4 might be a bad number since if 2 bits get flipped its already ambiguous). So if you go with 8, you get 8 times less ram and some fraction slower access time but a much more reliable data representation. You could probably do this both on the software level with a specialized compiler(allocate x amount more space for everything) or language implementation (write wrappers for data structures that allocate things this way). Or specialized hardware that has the same logical structure but does this in the firmware.
Perhaps it would help to know does it mean for the hardware to be "designed for this environment". How does it correct and/or indicates the presence of SEU errors ?
At one space exploration related project, we had a custom MCU, which would raise an exception/interrupt on SEU errors, but with some delay, i.e. some cycles may pass/instructions be executed after the one insn which caused the SEU exception.
Particularly vulnerable was the data cache, so a handler would invalidate the offending cache line and restart the program. Only that, due to the imprecise nature of the exception, the sequence of insns headed by the exception raising insn may not be restartable.
We identified the hazardous (not restartable) sequences (like lw $3, 0x0($2), followed by an insn, which modifies $2 and is not data-dependent on $3), and I made modifications to GCC, so such sequences do not occur (e.g. as a last resort, separating the two insns by a nop).
Just something to consider ...
If your hardware fails then you can use mechanical storage to recover it. If your code base is small and have some physical space then you can use a mechanical data store.
There will be a surface of material which will not be affected by radiation. Multiple gears will be there. A mechanical reader will run on all the gears and will be flexible to move up and down. Down means it is 0 and up means it is 1. From 0 and 1 you can generate your code base.
Use a cyclic scheduler. This gives you the ability to add regular maintenance times to check the correctness of critical data. The problem most often encountered is corruption of the stack. If your software is cyclical you can reinitialize the stack between cycles. Do not reuse the stacks for interrupt calls, setup a separate stack of each important interrupt call.
Similar to the Watchdog concept is deadline timers. Start a hardware timer before calling a function. If the function does not return before the deadline timer interrupts then reload the stack and try again. If it still fails after 3/5 tries you need reload from ROM.
Split your software into parts and isolate these parts to use separate memory areas and execution times (Especially in a control environment). Example: signal acquisition, prepossessing data, main algorithm and result implementation/transmission. This means a failure in one part will not cause failures through the rest of the program. So while we are repairing the signal acquisition the rest of tasks continues on stale data.
Everything needs CRCs. If you execute out of RAM even your .text needs a CRC. Check the CRCs regularly if you using a cyclical scheduler. Some compilers (not GCC) can generate CRCs for each section and some processors have dedicated hardware to do CRC calculations, but I guess that would fall out side of the scope of your question. Checking CRCs also prompts the ECC controller on the memory to repair single bit errors before it becomes a problem.
Use watchdogs for bootup no just once operational. You need hardware help if your bootup ran into trouble.
Firstly, design your application around failure. Ensure that as part of normal flow operation, it expects to reset (depending on your application and the type of failure either soft or hard). This is hard to get perfect: critical operations that require some degree of transactionality may need to be checked and tweaked at an assembly level so that an interruption at a key point cannot result in inconsistent external commands.
Fail fast as soon as any unrecoverable memory corruption or control flow deviation is detected. Log failures if possible.
Secondly, where possible, correct corruption and continue. This means checksumming and fixing constant tables (and program code if you can) often; perhaps before each major operation or on a timed interrupt, and storing variables in structures that autocorrect (again before each major op or on a timed interrupt take a majority vote from 3 and correct if is a single deviation). Log corrections if possible.
Thirdly, test failure. Set up a repeatable test environment that flips bits in memory psuedo-randomly. This will allow you to replicate corruption situations and help design your application around them.
Given supercat's comments, the tendencies of modern compilers, and other things, I'd be tempted to go back to the ancient days and write the whole code in assembly and static memory allocations everywhere. For this kind of utter reliability I think assembly no longer incurs a large percentage difference of the cost.
Here are huge amount of replies, but I'll try to sum up my ideas about this.
Something crashes or does not work correctly could be result of your own mistakes - then it should be easily to fix when you locate the problem. But there is also possibility of hardware failures - and that's difficult if not impossible to fix in overall.
I would recommend first to try to catch the problematic situation by logging (stack, registers, function calls) - either by logging them somewhere into file, or transmitting them somehow directly ("oh no - I'm crashing").
Recovery from such error situation is either reboot (if software is still alive and kicking) or hardware reset (e.g. hw watchdogs). Easier to start from first one.
If problem is hardware related - then logging should help you to identify in which function call problem occurs and that can give you inside knowledge of what is not working and where.
Also if code is relatively complex - it makes sense to "divide and conquer" it - meaning you remove / disable some function calls where you suspect problem is - typically disabling half of code and enabling another half - you can get "does work" / "does not work" kind of decision after which you can focus into another half of code. (Where problem is)
If problem occurs after some time - then stack overflow can be suspected - then it's better to monitor stack point registers - if they constantly grows.
And if you manage to fully minimize your code until "hello world" kind of application - and it's still failing randomly - then hardware problems are expected - and there needs to be "hardware upgrade" - meaning invent such cpu / ram / ... -hardware combination which would tolerate radiation better.
Most important thing is probably how you get your logs back if machine fully stopped / resetted / does not work - probably first thing bootstap should do - is a head back home if problematic situation is entcovered.
If it's possible in your environment also to transmit a signal and receive response - you could try out to construct some sort of online remote debugging environment, but then you must have at least of communication media working and some processor/ some ram in working state. And by remote debugging I mean either GDB / gdb stub kind of approach or your own implementation of what you need to get back from your application (e.g. download log files, download call stack, download ram, restart)
I've really read a lot of great answers!
Here is my 2 cent: build a statistical model of the memory/register abnormality, by writing a software to check the memory or to perform frequent register comparisons. Further, create an emulator, in the style of a virtual machine where you can experiment with the issue. I guess if you vary junction size, clock frequency, vendor, casing, etc would observe a different behavior.
Even our desktop PC memory has a certain rate of failure, which however doesn't impair the day to day work.
Context
The function BN_consttime_swap in OpenSSL is a thing of beauty. In this snippet, condition has been computed as 0 or (BN_ULONG)-1:
#define BN_CONSTTIME_SWAP(ind) \
do { \
t = (a->d[ind] ^ b->d[ind]) & condition; \
a->d[ind] ^= t; \
b->d[ind] ^= t; \
} while (0)
…
BN_CONSTTIME_SWAP(9);
…
BN_CONSTTIME_SWAP(8);
…
BN_CONSTTIME_SWAP(7);
The intention is that so as to ensure that higher-level bignum operations take constant time, this function either swaps two bignums or leaves them in place in constant time. When it leaves them in place, it actually reads each word of each bignum, computes a new word that is identical to the old word, and write that result back to the original location.
The intention is that this will take the same time as if the bignums had effectively been swapped.
In this question, I assume a modern, widespread architecture such as those described by Agner Fog in his optimization manuals. Straightforward translation of the C code to assembly (without the C compiler undoing the efforts of the programmer) is also assumed.
Question
I am trying to understand whether the construct above characterizes as a “best effort” sort of constant-time execution, or as perfect constant-time execution.
In particular, I am concerned about the scenario where bignum a is already in the L1 data cache when the function BN_consttime_swap is called, and the code just after the function returns start working on the bignum a right away. On a modern processor, enough instructions can be in-flight at the same time for the copy not to be technically finished when the bignum a is used. The mechanism allowing the instructions after the call to BN_consttime_swap to work on a is memory dependence speculation. Let us assume naive memory dependence speculation for the sake of the argument.
What the question seems to boil down to is this:
When the processor finally detects that the code after BN_consttime_swap read from memory that had, contrary to speculation, been written to inside the function, does it cancel the speculative execution as soon as it detects that the address had been written to, or does it allow itself to keep it when it detects that the value that has been written is the same as the value that was already there?
In the first case, BN_consttime_swap looks like it implements perfect constant-time. In the second case, it is only best-effort constant-time: if the bignums were not swapped, execution of the code that comes after the call to BN_consttime_swap will be measurably faster than if they had been swapped.
Even in the second case, this is something that looks like it could be fixed for the foreseeable future (as long as processors remain naive enough) by, for each word of each of the two bignums, writing a value different from the two possible final values before writing either the old value again or the new value. The volatile type qualifier may need to be involved at some point to prevent an ordinary compiler to over-optimize the sequence, but it still sounds possible.
NOTE: I know about store forwarding, but store forwarding is only a shortcut. It does not prevent a read being executed before the write it is supposed to come after. And in some circumstances it fails, although one would not expect it to in this case.
Straightforward translation of the C code to assembly (without the C compiler undoing the efforts of the programmer) is also assumed.
I know it's not the thrust of your question, and I know that you know this, but I need to rant for a minute. This does not even qualify as a "best effort" attempt to provide constant-time execution. A compiler is licensed to check the value of condition, and skip the whole thing if condition is zero. Obfuscating the setting of condition makes this less likely to happen, but is no guarantee.
Purportedly "constant-time" code should not be written in C, full stop. Even if it is constant time today, on the compilers that you test, a smarter compiler will come along and defeat you. One of your users will use this compiler before you do, and they will not be aware of the risk to which you have exposed them. There are exactly three ways to achieve constant time that I am aware of: dedicated hardware, assembly, or a DSL that generates machine code plus a proof of constant-time execution.
Rant aside, on to the actual architecture question at hand: assuming a stupidly naive compiler, this code is constant time on the µarches with which I am familiar enough to evaluate the question, and I expect it to broadly be true for one simple reason: power. I expect that checking in a store queue or cache if a value being stored matches the value already present and conditionally short-circuiting the store or avoiding dirtying the cache line on every store consumes more energy than would be saved in the rare occasion that you get to avoid some work. However, I am not a CPU designer, and do not presume to speak on their behalf, so take this with several tablespoons of salt, and please consult one before assuming this to be true.
This blog post, and the comments made by the author, Henry, on the subject of this question should be considered as authoritative as anyone should allowed to expect. I will reproduce the latter here for archival:
I didn’t think the case of overwriting a memory location with the same value had a practical use. I think the answer is that in current processors, the value of the store is irrelevant, only the address is important.
Out here in academia, I’ve heard of two approaches to doing memory disambiguation: Address-based, or value-based. As far as I know, current processors all do address-based disambiguation.
I think the current microbenchmark has some evidence that the value isn’t relevant. Many of the cases involve repeatedly storing the same value into the same location (particularly those with offset = 0). These were not abnormally fast.
Address-based schemes uses a store queue and a load queue to track outstanding memory operations. Loads check the store queue to for an address match (Should this load do store-to-load forwarding instead of reading from cache?), while stores check the load queue (Did this store clobber the location of a later load I allowed to execute early?). These checks are based entirely on addresses (where a store and load collided). One advantage of this scheme is that it’s a fairly straightforward extension on top of store-to-load forwarding, since the store queue search is also used there.
Value-based schemes get rid of the associative search (i.e., faster, lower power, etc.), but requires a better predictor to do store-to-load forwarding (Now you have to guess whether and where to forward, rather than searching the SQ). These schemes check for ordering violations (and incorrect forwarding) by re-executing loads at commit time and checking whether their values are correct. In these schemes, if you have a conflicting store (or made some other mistake) that still resulted in the correct result value, it would not be detected as an ordering violation.
Could future processors move to value-based schemes? I suspect they might. They were proposed in the mid-2000s(?) to reduce the complexity of the memory execution hardware.
The idea behind constant-time implementation is not to actually perform everything in constant time. That will never happen on an out-of-order architecture.
The requirement is that no secret information can be revealed by timing analysis.
To prevent this there are basically two requirements:
a) Do not use anything secret as a stop condition for a loop, or as a predicate to a branch. Failing to do so will open you to a branch prediction attack https://eprint.iacr.org/2006/351.pdf
b) Do not use anything secret as an index to memory access. This leads to cache timing attacks http://www.daemonology.net/papers/htt.pdf
As for your code: assuming that your secret is "condition" and possibly the contents of a and b the code is perfectly constant time in the sense that its execution does not depend on the actual contents of a, b and condition. Of course the locality of a and b in memory will affect the execution time of the loop, but not the CONTENTS which are secret.
That is assuming of course condition was computed in a constant time manner.
As for C optimizations: the compiler can only optimize code based on information it knows. If "condition" is truly secret the compiler should not be able to discern it contents and optimize. If it can be deducted from your code then the compiler will most likely make optimization for the 0 case.