I've spent my afternoon reading up on processor caches after reading about the effect power of twos can have on cache conflicts. Now I wish to apply this new knowledge to my memory allocator for multi-threaded programs. However, I don't fully understand it yet.
I was under the impression that processors loved powers of two, so my allocator rounds requested sizes to their next power of two and then slices pages into multiples of this size and hands them out. When a page is full, it simply maps a new page and slices it up the same way. This leads to very similar and predictable offsets into pages.
To what extent should I adapt my allocator to avoid this issue? For example, should I try to randomize addresses slightly or am I screwed for using powers of two in the first place?
Thanks!
Until you have uncontrovertible proof that this is performance critical, just leave it be. The extra complication will most probably not be worth it.
Everybody should read (and understand!) Bentley's "Writing efficient programs" (sadly out of print now, his "Programming Pearls" contains a summary, and is well worth a read too).
Before embarking on a code-optimization bout, make sure it is worth it. If the performance is adequate, there are better uses of your time. Yes, you have to measure first.
Measure where the cost is being spent. Programmers are notoriously bad at guessing where the costs are
The most performance gains come from restating the problem (sometimes it is enough to solve a problem that is faster to solve), then overall organization of the system, next better algorithms/data structures; and at the very, very end detail optimizations like the one considered here.
Your friendly compiler, given a bit of prodding in the direction of "generate good code" will today generate much better code than an experienced assembly language programmer when given similar (full function scale) tasks. Most local source code reorganizations "for performance" are either moot (the compiler would have done so on its own) or deleterious (the compiler will recognize and rewrite the usual code sequences, unusual code can confuse it to do nothing or generate bad code).
Programmer time (writing, debugging, maintaining) is much more valuable than a few microseconds of computer time here and there, except for extremely unusual circumstances. Write the simplest code that does the job, rework only if experience shows it is worthwile.
Is there some open source tiny GC implementation (preferably as one C source file)?
Google search provides tinygc.sourceforge.net :)
I've got some prototype code that might give you a head start. If all your pointers are "managed" through your interface, you can chop up a heap in any convenient way and use the classic algorithms from 70s dissertations. My adventures with a postscript garbage collector began here.
On reading through it again, the code may not be what you're looking for. It's designed to run on top of an OS. In particular, it uses relative integer locations as much as possible to allow the entire memory space to be moved by the OS if needed for a reallocation. I imagine you don't need to do that (although it guarantees that internal relocations are ok, too). But the code should show that a garbage collector doesn't have to be horribly complicated. It's just a tree traversal. It's futzing with some bits and following some pointers. Keep it simple. You can do it.
I need a simple (LRU) cache which should run in-process. I found memcached, which looks great but there does not seem to be an easy way to host it in-process. I don't need a distributed cache, just a simple key/value store and some kind of LRU behaviour and some nice allocator to limit fragmentation, as the entry size varies a lot (few bytes -- few kilobytes.) There must be surely an existing implementation of such a thing? Should be C or C++.
I hate to answer this way, but it would be fairly simple to implement yourself.
Allocator. Use malloc and free. They do work, and they work well. This also makes it easier to interface with the rest of your program.
Mutex -> hash table, tree, or trie. You can use a linked list to track LRU. Don't try to do fancy lockless stuff.
Should weigh less than a couple hundred lines, knock it out in a good solid day.
I've had success using commoncache but the project doesn't appear to have any activity and issues raised (with patches) by my colleague are still unaddressed...
i have a general question about programming of parallel algorithms in C. Lets assume that our task is to implement some matrix algorithms with MPI and/or OpenMP. There are some situations, like false sharing in OpenMP or in MPI where the communications arise in dependence of the matrix dimension (columns cyclic distrubuted among processes), which cause some problems . Would it be a good and a common attempt to solve this situations by, for example, transposing the matrix, because this would reduce the necessary communications or even avoiding the false sharing problem? After that you would undo the transposition. Of course, assuming that this would lead to a much better speed up.
I dont think that this would be very cunning and more of a lazy way to do this. But im curious to read some opions about this.
Let's start with the first question first: can it make sense to transpose? The answer is, it depends, and you can estimate whether it will improve things or not.
The transposition/retransposition with impose a one-time memory bandwidth cost of 2*(going through memory the fast way) + 2*(going through memory the slow way) where those memory operations are literally memory operations in the multicore case, or network communications in the distributed memory case. You're going to be reading a matrix in the fast way and putting it into memory the slow way. (You can make this, essentially, 4*(going through memory the fast way) by reading the matrix in one cache-sized block at a time, transposing in cache, and writing out in order).
Whether or not that's a win or not depends on how many times you'll be accessing the array. If you would have been hitting the entire non-transposed array 4 times with memory access in the "wrong" direction, then you will clearly win by doing the two transposes. If you'd only be going through the non-transposed array once in the wrong direction, then you almost certainly won't win by doing the transposition.
As to the larger question, #AlexandreC is absolutely right here -- trying to implement your own linear algebra routines is madness. Take a look at, eg, How To Write Fast Numerical Code, figure 3; there can be factors of 40 in performance between naive and highly-tuned (say) GEMM operations. These things are highly memory-bandwidth limited, and in parallel that means network limited. By far, best is to use existing tools.
For multicore linear algebra, existing libraries include
Atlas
Plasma
Flame
For MPI implementations, there are
BLACS
Scalapack
or complete solver environments like
PETSc
Trilinos
I don't know that you'd throw the transpose away the second that you completed the operation, but yes this is a valid mechanism to increase parallelism.
I am not an expert; I've only read a little bit about this topic, and even that was for SIMD architectures, so please take my opinion lightly... but I think the usual mechanism is to lay your structures out in memory to match the machine (so you'd transpose a large matrix to line up better with your vectors and increase the dependency distance in your loops), and then you also build an indexing structure of pointers around that so that you can quickly access individual elements in the transpose differently. This gets more difficult to do as your input changes more dynamically.
I dont think that this would be very cunning and more of a lazy way to do this.
Lazy solutions are usually better than "cunning" ones, because they tend to be more simple and straightforward. They're therefore easier to implement, document, understand and maintain. Indeed, laziness is arguably one of the greatest virtues a programmer can have. As long as the program produces correct results at acceptable speeds, nobody should care how elegantly you solved the problem (including you).
I know about the existance of question such as this one and this one. Let me explain.
Afet reading Joel's article Back to Basics and seeing many similar questions on SO, I've begun to wonder what are specific examples of situations where knowing stuff like C can make you a better high level programmer.
What I want to know is if there are many examples of this. Many times, the answer to this question is something like "Knowing C gives you a better feel of what's happening under the covers" or "You need a solid foundation for your program", and these answers don't have much meaning. I want to understand the different specific ways in which you will benefit from knowing low level concepts,
Joel gave a couple of examples: Binary databases vs XML, and strings. But two examples don't really justify learning C and/or Assembly. So my question is this: What specific examples are there of knowing C making you a better high level programmer?
My experience with teaching students and working with people who only studied high-level languages is that they tend to think at a certain high level of abstraction, and they assume that "everything comes for free". They can become very competent programmers, but eventually they have to deal with some code that has performance issues and then it comes to bite them.
When you work a lot with C, you do think about memory allocation. You often think about memory layout (and cache locality if that's an issue). You understand how and why certain graphics operations just cost a lot. How efficient or inefficient certain socket behaviors are. How buffers work, etc. I feel that using the abstractions in a higher level language when you do know how it is implemented below the covers sometimes gives you "that extra secret sauce" when thinking about performance.
For example, Java has a garbage collector and you can't directly assign things to memory directly. And yet, you can make certain design choices (e.g., with custom data structures) that affect performance because of the same reasons this would be an issue in C.
Also, and more generally, I feel that it is important for a power programmer to not only know big-O notation (which most schools teach), but that in real-life applications the constant is also important (which schools try to ignore). My anecdotal experience is that people with skills in both language levels tend to have a better understanding of the constant, perhaps because of what I described above.
In addition, many higher level systems that I have seen interface with lower level libraries and infrastructures. For instance, some communications, databases or graphics libraries. Some drivers for certain devices, etc. If you are a power programmer, you may eventially have to venture out there and it helps to at least have an idea of what is going on.
Knowing low level stuff can help a lot.
To become a racing driver, you have to learn and understand the basic physics of how tyres grip the road. Anyone can learn to drive pretty fast, but you need a good understanding of the "low level" stuff (forces and friction, racing lines, fine throttle and brake control, etc) to get those last few percent of performance that will allow you to win the race.
For example, if you understand how the CPU architecture works in your computer, you can write code that works better with it (e.g. if you know you have a certain CPU cache size or a certain number of bytes in each CPU cache line, you can arrange your data structures and the way that you access them to make the best use of the cache - for example, processing many elements of an array in order is often faster than processing random elements, due to the CPU cache). If you have a multi-core computer, then understanding how low level techniques like threading work can gave huge benefits (just as not understanding the low level can lead to disaster in threading).
If you understand how Disk I/O and caching works, you can modify file operations to work well with it (e.g. if you read from one file and write to another, working on large batches of data in RAM can help reduce I/O contention between the reading and writing phases of your code, and vastly improve throughput)
If you understand how virtual functions work, you can design high-level code that uses virtual functions well. If used incorrectly they can severely hamper performance.
If you understand how drawing is handled, you can use clever tricks to improve drawing speed. e.g. You can draw a chessboard by alternately drawing 64 white and black squares. But it is often faster to draw 32 white sqares and then 32 black ones (because you only have to change the drawing colour twice instead of 64 times). But you can actually draw the whole board black, then XOR 4 stripes across the board and 4 stripes down the board in white, and this can be much faster still (2 colour changes, and only 9 rectangles to draw instead of 64). This chessboard trick teaches you a very important programming skill: Lateral thinking. By designing your algorithm well, you can often make a big difference to how well your program operates.
Understanding C, or for that matter, any low level programming language, gives you an opportunity to understand things like memory usage (i.e. why is it a bad thing to create several million heavy objects), how pointers/object references work, etc.
The problem is that as we've created ever increasing levels of abstraction, we find ourselves doing a lot of 'lego block' programming, without understanding how the legos actually function. And by having almost infinite resources, we start treating memory and resources like water, and tend to solve problems by throwing more iron at the situation.
While not limited to C, there's a tremendous benefit to working at a low level with much smaller, memory constrained systems like the Arduino or old-school 8-bit processors. It lets you experience close to the metal coding in a much more approachable package, and after spending time squeezing apps into 512K, you will find yourself applying these skills at a larger level within your day to day programming.
So the language itself is not important, but having a deeper appreciation for how all of the bits come together, and how to work effectively at a level closer to the hardware is a set of skills beneficial to any software developer.
For one, knowing C helps you understand how memory works in the OS and in other high level languages. When your C# or Java program balloons on memory usage, understanding that references (which are basically just pointers) take memory too, and understand how many of the data structures are implemented (which you get from making your own in C) helps you understand that your dictionary is reserving huge amounts of memory that aren't actually used.
For another, knowing C can help you understand how to make use of lower level operating system features. You don't need this often, but sometimes you may need memory mapped files, or to use marshalling in C#, and C will greatly help understand what you're doing when that happens.
I think C has also helped my understanding of network protocols, but I can't put my finger on specific examples. I was reading another SO question the other day where someone was complaining about how C's bit-fields are 'basically useless' and I was thinking how elegantly C bit fields represent low-level network protocols. High level languages dealing with structures of bits always end up a mess!
In general, the more you know, the better programmer you will be.
However, sometimes knowing another language, such as C, can make you do the wrong thing, because there might be an assumption that is not true in a higher-level language (such as Python, or PHP). For example, one might assume that finding the length of a list might be O(N) where N is the length of the list. However, this is probably not the case in many high-level language instances. In Python, for most list-like things the cost is O(1).
Knowing more about the specifics of a language will help, but knowing more in general might lead one to make incorrect assumptions.
Just "knowing" C would not make you better.
But, if you understand the whole thing, how native binaries work, how does CPU work with it, what are architecture limitations, you may write a code which is easier for CPU.
For example, how L1/L2 caches affect your work, and how should you write your code to have more hits in L1/L2 caches. When working with C/C++ and doing heavy optimizations, you will have to go down to that kind of things.
It isn't so much knowing C as it is that C is closer to the bare metal than many other languages. You need to be more aware of how to allocate/deallocate memory because you have to do it yourself. Doing it yourself helps you understand the implications of many decisions that you make.
To me any language is acceptable as long as you understand how the compiler/interpreter (basically) maps your code onto the machine. It's a bit easier to do in a language that exposes this directly, but you should be able to, with a bit of reading, figure out how memory is allocated and organized, what sort of indexing patterns are more optimal than others, what constructs are more efficient for particular applications, etc.
More important, I think, is a good understanding of operating systems, memory architectures, and algorithms. If you understand how your algorithm works, why it would be better to choose one algorithm or data structure over another (e.g., HashSet vs. List), and how your code maps onto the machine, it shouldn't matter what language you are using.
This is my experience of how I learnt and taught myself programming, specifically, understanding C, this is going back to early 1990's so may be a bit antique, but the passion and the drive is important:
Learn to understand the low level principles of the computer, such as EGA/VGA programming, here's a link to the Simtel archive on the C programmer's guide to the PC.
Understanding how TSR's work
Download the whole archive of Bob Stout's snippets which is a big collection of C code that does one thing only - study them and understand it, not alone that, the collection of snippets strives to be portable.
Browse at the International Obfuscated C Code Contest (IOCCC) online, and see how the C code can be abused and understand the intracies of the language. The worst code abuse is the winner! Download the archives and study them.
Like myself, I loved the infamous Ponzo's C Tutorial which helped me immensely, unfortunately, the archive is very hard to find. If anyone knows of where to obtain them, please leave a comment and I will amend this answer to include the link. There is another one that I can remember - Coronado's [Generic?] C Tutorial, again, my memory on this one is hazy...
Look at Dr. Dobb's journal and C User Journal here - I do not know if you can still get them in print but they were a classic, can remember the feeling of holding a printed copy in my hand and tearing off home to type in the code to see what happens!
Grab an ancient copy of Turbo C v2 which I believe you can get from borland.com and just play with 16bit C programming to get a feel and mess with the pointers...sure it is ancient and old but playing with pointers on it is fine.
Understand and learn Pointers, link here to the legacy Simtel.net - a crucial link to achieving C Guru'ship for want of a better word, also you will find a host of downloads pertaining to the C programming language - I remember actually ordering the Simtel CD Archive and looking for the C stuff...
A couple of things that you have to deal directly with in C that other languages abstract away from you include explicit memory management (malloc) and dealing directly with pointers.
My girlfriend is one semester from graduating MIT (where they mainly use Java, Scheme, and Python) with a Computer Science degree, and she is currently working at a company whose codebase is in C++. For the first few days she had a difficult time understanding all the pointers/references/etc.
On the other hand, I found moving from C++ to Java very easy, because I was never confused about pass-references-by-value vs pass-by-reference.
Similarly, in C/C++ it is much more apparent that primitives are just the compiler treating the same sets of bits in different ways, as opposed to a language like Python or Ruby where everything is an object with its own distinct properties.
A simple (not entirely realistic) example to illustrate some of the advice above. Consider the seemingly harmless
while(true)
for(Iterator iter = foo.iterator(); iter.hasNext();)
bar.doSomething( iter.next() )
or the even higher level
while(true)
for(Baz b: foo)
bar.doSomething(b)
A possible problem here is that each time round the while loop a new object (the iterator) is created. If all you care about is programmer convenience, then the latter is definitely better. But if the loop has to be efficient or the machine is resource constrained then you are pretty much at the mercy of the designers of your high level language.
For example, a typical complaint for doing high-performance Java is having execution stop while garbage (such as all those allocated Iterator objects) is reclaimed. Not very good if your software is charged with tracking incoming missiles, auto-piloting a passenger jet, or just not leaving the user wondering why the GUI has stopped responding.
One possible solution (still in the higher-level language) would be to weaken the convenience of the iterator to something like
Iterator iter = new Iterator();
while(true)
for(foo.initAlreadyAllocatedIterator(iter); iter.hasNext();)
bar.doSomething(iter.next())
But this would only make sense if you had some idea about memory allocation...otherwise it just looks like a nasty API. Convenience always costs somewhere, and knowing lower-level stuff can help you identify and mitigate those costs.