Basically I'm building a bit reader, slightly before the buffer has been exhausted, I'd like to copy whatever is left of the array to the beginning of the same array, and then zero everything after the copy, and fill the rest with data from the input file.
I'm only trying to use the standard library for portability reasons.
Also, I was profiling my bit reader earlier and Instruments says it was taking like 28 milliseconds to do all of this, is it supposed to take that long?
Code removed
I recommend using memmove for the copy. It has a signature and functionality identical to memcpy, except that it is safe for copying between overlapping regions (which is what you're describing).
For the zero-fill, memset is usually adequate. On the occasion where null pointers aren't represented using an underlying sequence of zeros, for example, you'll need to roll your own using assignment depending upon the type.
For this reason you might want to hide the memmove and memset operations behind abstraction, for example:
#include <string.h>
void copy_int(int *destination, int *source, size_t size) {
memmove(destination, source, size * sizeof *source);
}
void zero_int(int *seq, size_t size) {
memset(seq, 0, size * sizeof *seq);
}
int main(void) {
int array[] = { 0, 1, 2, 3, 4, 5 };
size_t index = 2
, size = sizeof array / sizeof *array - index;
copy_int(array, array + index, size);
zero_int(array + size, index);
}
Should either memmove or memset become unsuitable for usecases in the future, it'll then be simple to drop in your own copy/zero loops.
As for your strange profiler results, I suppose it might be possible that you're using some archaic (or grossly underclocked) implementation, or trying to copy huge arrays... Otherwise, 28 milliseconds does seem quite absurd. Nonetheless, your profiler would have surely identified that this memmove and memset isn't a significant bottleneck in an a program that performs actual I/O work, right? The I/O must surely be the bottleneck, right?
If the memmove+memset is indeed a bottleneck, you could try implementing a circular array to avoid the copies. For example, the following code attempts to find needle in the figurative haystack that is input_file...
Otherwise, if the I/O is a bottleneck, there are tweaks that can be applied to reduce that. For example, the following code uses setvbuf to suggest that the underlying implementation attempt to use an underlying buffer to read chunks of the file, despite the code using fgetc to read one character at a time.
void find_match(FILE *input_file, char const *needle, size_t needle_size) {
char input_array[needle_size];
size_t sz = fread(input_array, 1, needle_size, input_file);
if (sz != needle_size) {
// No matches possible
return;
}
setvbuf(input_file, NULL, _IOFBF, BUFSIZ);
unsigned long long pos = 0;
for (;;) {
size_t cursor = pos % needle_size;
int tail_compare = memcmp(input_array, needle + needle_size - cursor, cursor),
head_compare = memcmp(input_array + cursor, needle, needle_size - cursor);
if (head_compare == 0 && tail_compare == 0) {
printf("Match found at offset %llu\n", pos);
}
int c = fgetc(input_file);
if (c == EOF) {
break;
}
input_array[cursor] = c;
pos++;
}
}
Notice how there's no memmove (or zeroing, FWIW) necessary here? We simply operate as though the start of the array is at cursor, the end at cursor - 1 and we wrap by modulo needle_size to ensure there's no overflow/underflow. Then, after each insertion we simply increment the cursor...
Related
Below I have extracted a piece of code from a project over which I ran a static analysis tool looking for security flaws. It flagged this code as being susceptible to an integer overflow/wraparound flaw as explained here:
https://cwe.mitre.org/data/definitions/190.html
Here is the relevant code:
#define STRMAX 16
void bar() {
// ... struct malloc'd elsewhere
mystruct->string = malloc(STRMAX * sizeof(char));
memset(mystruct->string, '\0', STRMAX);
strncpy(mystruct->string,"H3C19H1E4XAA9MQ",STRMAX); // 15 character string
mystruct->baz = malloc(3 * sizeof(char*));
memset(mystruct->baz, '\0', 3);
if (strlen(mystruct->string) > 0) {
strncpy(mystruct->baz,&(mystruct->string[0]),2);
}
mystruct->quux = malloc(19 * sizeof(char));
memset(mystruct->quux, '\0', 19);
for(int i = 0; i < 19; i++) {
mystruct->quux[i] = 'a';
}
foo(struct);
}
void foo(Mystruct *struct) {
// this was flagged
size_t input_len = strlen(mystruct->baz) + strlen(mystruct->quux);
// this one too but it flows from the first I think.
char *input = malloc((input_len + 1) * sizeof(char));
memset(input, '\0', input_len + 1);
strncpy(input, mystruct->baz, strlen(mystruct->baz));
strncat(input, mystruct->quux, strlen(mystruct->quux));
// ...
}
So in other words, there are three members of a struct that are explicitly bounded in one function and then used to create a variable in another one based on the size of the struct's members.
The analyzer flagged the first line of foo in particular. My question is, did it correctly flag this? If so, what would be a simple way to mitigate this? I'm on a platform that doesn't have a BSD-style reallocate function by default.
PS: I realize that strncpy(foo, bar, strlen(bar)) is somewhat frivolous in memory terms.
size_t input_len = strlen(struct->baz) + strlen(struct->quux); can theoretically wraparound so the analyser is right. If strlen(struct->quux) is larger than SIZE_MAX - strlen(struct->baz) it will wraparound
If input_len == SIZE_MAX then input_len + 1 can wraparound as well.
When you add two string lengths, the result can exceed the maximum value of the size_t type. For instance, if SIZE_MAX were 100 (I know this is not realistic, it's just for example purposes), and your strings were 70 and 50 characters long, the total would wrap around to 20, which is not the correct result.
This is rarely a real concern, since the actual maximum is usually very large (the C standard allows it to be as low as 65535, but you're only likely to run into this on microcontrollers), and in practice strings are not even close to the limit, so most programmers would just ignore this problem. Protecting against this overflow gets complicated, although it's possible if you really need to.
The last time update: my classmate uses fread() to read about one third of the whole file into a string, this can avoid lacking of memory. Then process this string, separate this string into your data structure. Notice, you need to care about one problem: at the end of this string, these last several characters may cannot consist one whole number. Think about one way to detect this situation so you can connect these characters with the first several characters of the next string.
Each number is corresponding to different variable in your data structure. Your data structure should be very simple because each time if you insert your data into one data structure, it is very slow. The most of time is spent on inserting data into data structure. Therefore, the fastest way to process these data is: using fread() to read this file into a string, separate this string into different one-dimensional arrays.
For example(just an example, not come from my project), I have a text file, like:
72 24 20
22 14 30
23 35 40
42 29 50
19 22 60
18 64 70
.
.
.
Each row is one person's information. The first column means the person's age, the second column is his deposit, the second is his wife's age.
Then we use fread() to read this text file into string, then I use stroke() to separate it(you can use faster way to separate it).
Don't use data structure to store the separated data!
I means, don't do like this:
struct person
{
int age;
int deposit;
int wife_age;
};
struct person *my_data_store;
my_data_store=malloc(sizeof(struct person)*length_of_this_array);
//then insert separated data into my_data_store
Don't use data structure to store data!
The fastest way to store your data is like this:
int *age;
int *deposit;
int *wife_age;
age=(int*)malloc(sizeof(int)*age_array_length);
deposit=(int*)malloc(sizeof(int)*deposit_array_length);
wife_age=(int*)malloc(sizeof(int)*wife_array_length);
// the value of age_array_length,deposit_array_length and wife_array_length will be known by using `wc -l`.You can use wc -l to get the value in your C program
// then you can insert separated data into these arrays when you use `stroke()` to separate them.
The second update: The best way is to use freed() to read part of the file into a string, then separate these string into your data structure. By the way, don't use any standard library function which can format string into integer , that's to slow, like fscanf() or atoi(), we should write our own function to transfer a string into n integer. Not only that, we should design a more simpler data structure to store these data. By the way, my classmate can read this 1.7G file within 7 seconds. There is a way can do this. That way is much better than using multithread. I haven't see his code, after I see his code, I will update the third time to tell you how could hi do this. That will be two months later after our course finished.
Update: I use multithread to solve this problem!! It works! Notice: don't use clock() to calculate the time when using multithread, that's why I thought the time of execution increases.
One thing I want to clarify is that, the time of reading the file without storing the value into my structure is about 20 seconds. The time of storing the value into my structure is about 60 seconds. The definition of "time of reading the file" includes the time of read the whole file and store the value into my structure. the time of reading the file = scan the file + store the value into my structure. Therefore, have some suggestions of storing value faster ? (By the way, I don't have control over the inout file, it is generated by our professor. I am trying to use multithread to solve this problem, if it works, I will tell you the result.)
I have a file, its size is 1.7G.
It looks like:
1 1427826
1 1427827
1 1750238
1 2
2 3
2 4
3 5
3 6
10 7
11 794106
.
.
and son on.
It has about ten millions of lines in the file. Now I need to read this file and store these numbers in my data structure within 15 seconds.
I have tried to use freed() to read whole file and then use strtok() to separate each number, but it still need 80 seconds. If I use fscanf(), it will be slower. How do I speed it up? Maybe we cannot make it less than 15 seconds. But 80 seconds to read it is too long. How to read it as fast as we can?
Here is part of my reading code:
int Read_File(FILE *fd,int round)
{
clock_t start_read = clock();
int first,second;
first=0;
second=0;
fseek(fd,0,SEEK_END);
long int fileSize=ftell(fd);
fseek(fd,0,SEEK_SET);
char * buffer=(char *)malloc(sizeof(char)*fileSize);
char *string_first;
long int newFileSize=fread(buffer,1,fileSize,fd);
char *string_second;
while(string_first!=NULL)
{
first=atoi(string_first);
string_second=strtok(NULL," \t\n");
second=atoi(string_second);
string_first=strtok(NULL," \t\n");
max_num= first > max_num ? first : max_num ;
max_num= second > max_num ? second : max_num ;
root_level=first/NUM_OF_EACH_LEVEL;
leaf_addr=first%NUM_OF_EACH_LEVEL;
if(root_addr[root_level][leaf_addr].node_value!=first)
{
root_addr[root_level][leaf_addr].node_value=first;
root_addr[root_level][leaf_addr].head=(Neighbor *)malloc(sizeof(Neighbor));
root_addr[root_level][leaf_addr].tail=(Neighbor *)malloc(sizeof(Neighbor));
root_addr[root_level][leaf_addr].g_credit[0]=1;
root_addr[root_level][leaf_addr].head->neighbor_value=second;
root_addr[root_level][leaf_addr].head->next=NULL;
root_addr[root_level][leaf_addr].tail=root_addr[root_level][leaf_addr].head;
root_addr[root_level][leaf_addr].degree=1;
}
else
{
//insert its new neighbor
Neighbor *newNeighbor;
newNeighbor=(Neighbor*)malloc(sizeof(Neighbor));
newNeighbor->neighbor_value=second;
root_addr[root_level][leaf_addr].tail->next=newNeighbor;
root_addr[root_level][leaf_addr].tail=newNeighbor;
root_addr[root_level][leaf_addr].degree++;
}
root_level=second/NUM_OF_EACH_LEVEL;
leaf_addr=second%NUM_OF_EACH_LEVEL;
if(root_addr[root_level][leaf_addr].node_value!=second)
{
root_addr[root_level][leaf_addr].node_value=second;
root_addr[root_level][leaf_addr].head=(Neighbor *)malloc(sizeof(Neighbor));
root_addr[root_level][leaf_addr].tail=(Neighbor *)malloc(sizeof(Neighbor));
root_addr[root_level][leaf_addr].head->neighbor_value=first;
root_addr[root_level][leaf_addr].head->next=NULL;
root_addr[root_level][leaf_addr].tail=root_addr[root_level][leaf_addr].head;
root_addr[root_level][leaf_addr].degree=1;
root_addr[root_level][leaf_addr].g_credit[0]=1;
}
else
{
//insert its new neighbor
Neighbor *newNeighbor;
newNeighbor=(Neighbor*)malloc(sizeof(Neighbor));
newNeighbor->neighbor_value=first;
root_addr[root_level][leaf_addr].tail->next=newNeighbor;
root_addr[root_level][leaf_addr].tail=newNeighbor;
root_addr[root_level][leaf_addr].degree++;
}
}
Some suggestions:
a) Consider converting (or pre-processing) the file into a binary format; with the aim to minimise the file size and also drastically reduce the cost of parsing. I don't know the ranges for your values, but various techniques (e.g. using one bit to tell if the number is small or large and storing the number as either a 7-bit integer or a 31-bit integer) could halve the file IO (and double the speed of reading the file from disk) and slash parsing costs down to almost nothing. Note: For maximum effect you'd modify whatever software created the file in the first place.
b) Reading the entire file into memory before you parse it is a mistake. It doubles the amount of RAM required (and the cost of allocating/freeing) and has disadvantages for CPU caches. Instead read a small amount of the file (e.g. 16 KiB) and process it, then read the next piece and process it, and so on; so that you're constantly reusing the same small buffer memory.
c) Use parallelism for file IO. It shouldn't be hard to read the next piece of the file while you're processing the previous piece of the file (either by using 2 threads or by using asynchronous IO).
d) Pre-allocate memory for the "neighbour" structures and remove most/all malloc() calls from your loop. The best possible case is to use a statically allocated array as a pool - e.g. Neighbor myPool[MAX_NEIGHBORS]; where malloc() can be replaced with &myPool[nextEntry++];. This reduces/removes the overhead of malloc() while also improving cache locality for the data itself.
e) Use parallelism for storing values. For example, you could have multiple threads where the first thread handles all the cases where root_level % NUM_THREADS == 0, the second thread handles all cases where root_level % NUM_THREADS == 1, etc.
With all of the above (assuming a modern 4-core CPU), I think you can get the total time (for reading and storing) down to less than 15 seconds.
My suggestion would be to form a processing pipeline and thread it. Reading the file is an I/O bound task and parsing it is CPU bound. They can be done at the same time in parallel.
There are several possibilities. You'll have to experiment.
Exploit what your OS gives you. If Windows, check out overlapped io. This lets your computation proceed with parsing one buffer full of data while the Windows kernel fills another. Then switch buffers and continue. This is related to what #Neal suggested, but has less overhead for buffering. Windows is depositing data directly in your buffer through the DMA channel. No copying. If Linux, check out memory mapped files. Here the OS is using the virtual memory hardware to do more-or-less what Windows does with overlapping.
Code your own integer conversion. This is likely to be a bit faster than making a clib call per integer.
Here's example code. You want to absolutely limit the number of comparisons.
// Process one input buffer.
*end_buf = ' '; // add a sentinel at the end of the buffer
for (char *p = buf; p < end_buf; p++) {
// somewhat unsafe (but fast) reliance on unsigned wrapping
unsigned val = *p - '0';
if (val <= 9) {
// Found start of integer.
for (;;) {
unsigned digit_val = *p - '0';
if (digit_val > 9) break;
val = 10 * val + digit_val;
p++;
}
... do something with val
}
}
Don't call malloc once per record. You should allocate blocks of many structs at a time.
Experiment with buffer sizes.
Crank up compiler optimizations. This is the kind of code that benefits greatly from excellent code generation.
Yes, standard library conversion functions are surprisingly slow.
If portability is not a problem, I'd memory-map the file. Then, something like the following C99 code (untested) could be used to parse the entire memory map:
#include <stdlib.h>
#include <errno.h>
struct pair {
unsigned long key;
unsigned long value;
};
typedef struct {
size_t size; /* Maximum number of items */
size_t used; /* Number of items used */
struct pair item[];
} items;
/* Initial number of items to allocate for */
#ifndef ITEM_ALLOC_SIZE
#define ITEM_ALLOC_SIZE 8388608
#endif
/* Adjustment to new size (parameter is old number of items) */
#ifndef ITEM_REALLOC_SIZE
#define ITEM_REALLOC_SIZE(from) (((from) | 1048575) + 1048577)
#endif
items *parse_items(const void *const data, const size_t length)
{
const unsigned char *ptr = (const unsigned char *)data;
const unsigned char *const end = (const unsigned char *)data + length;
items *result;
size_t size = ITEMS_ALLOC_SIZE;
size_t used = 0;
unsigned long val1, val2;
result = malloc(sizeof (items) + size * sizeof (struct pair));
if (!result) {
errno = ENOMEM;
return NULL;
}
while (ptr < end) {
/* Skip newlines and whitespace. */
while (ptr < end && (*ptr == '\0' || *ptr == '\t' ||
*ptr == '\n' || *ptr == '\v' ||
*ptr == '\f' || *ptr == '\r' ||
*ptr == ' '))
ptr++;
/* End of data? */
if (ptr >= end)
break;
/* Parse first number. */
if (*ptr >= '0' && *ptr <= '9')
val1 = *(ptr++) - '0';
else {
free(result);
errno = ECOMM; /* Bad data! */
return NULL;
}
while (ptr < end && *ptr >= '0' && *ptr <= '9') {
const unsigned long old = val1;
val1 = 10UL * val1 + (*(ptr++) - '0');
if (val1 < old) {
free(result);
errno = EDOM; /* Overflow! */
return NULL;
}
}
/* Skip whitespace. */
while (ptr < end && (*ptr == '\t' || *ptr == '\v'
*ptr == '\f' || *ptr == ' '))
ptr++;
if (ptr >= end) {
free(result);
errno = ECOMM; /* Bad data! */
return NULL;
}
/* Parse second number. */
if (*ptr >= '0' && *ptr <= '9')
val2 = *(ptr++) - '0';
else {
free(result);
errno = ECOMM; /* Bad data! */
return NULL;
}
while (ptr < end && *ptr >= '0' && *ptr <= '9') {
const unsigned long old = val2;
val1 = 10UL * val2 + (*(ptr++) - '0');
if (val2 < old) {
free(result);
errno = EDOM; /* Overflow! */
return NULL;
}
}
if (ptr < end) {
/* Error unless whitespace or newline. */
if (*ptr != '\0' && *ptr != '\t' && *ptr != '\n' &&
*ptr != '\v' && *ptr != '\f' && *ptr != '\r' &&
*ptr != ' ') {
free(result);
errno = ECOMM; /* Bad data! */
return NULL;
}
/* Skip the rest of this line. */
while (ptr < end && *ptr != '\n' && *ptr != '\r')
ptr++;
}
/* Need to grow result? */
if (used >= size) {
items *const old = result;
size = ITEMS_REALLOC_SIZE(used);
result = realloc(result, sizeof (items) + size * sizeof (struct pair));
if (!result) {
free(old);
errno = ENOMEM;
return NULL;
}
}
result->items[used].key = val1;
result->items[used].value = val2;
used++;
}
/* Note: we could reallocate result here,
* if memory use is an issue.
*/
result->size = size;
result->used = used;
errno = 0;
return result;
}
I've used a similar approach to load molecular data for visualization. Such data contains floating-point values, but precision is typically only about seven significant digits, no multiprecision math needed. A custom routine to parse such data beats the standard functions by at least an order of magnitude in speed.
At least the Linux kernel is pretty good at observing memory/file access patterns; using madvise() also helps.
If you cannot use a memory map, then the parsing function would be a bit different: it would append to an existing result, and if the final line in the buffer is partial, it would indicate so (and the number of chars not parsed), so that the caller can memmove() the buffer, read more data, and continue parsing. (Use 16-byte aligned addresses for reading new data, to maximize copy speeds. You don't necessarily need to move the unread data to the exact beginning of the buffer, you see; just keep the current position in the buffered data.)
Questions?
First, what's your disk hardware? A single SATA drive is likely to be topped out at 100 MB/sec. And probably more like 50-70 MB/sec. If you're already moving data off the drive(s) as fast as you can, all the software tuning you do is going to be wasted.
If your hardware CAN support reading faster? First, your read pattern - read the whole file into memory once - is the perfect use-case for direct IO. Open your file using open( "/file/name", O_RDONLY | O_DIRECT );. Read to page-aligned buffers (see man page for valloc()) in page-sized chunks. Using direct IO will cause your data to bypass double buffering in the kernel page cache, which is useless when you're reading that much data that fast and not re-reading the same data pages over and over.
If you're running on a true high-performance file system, you can read asynchronously and likely faster with lio_listio() or aio_read(). Or you can just use multiple threads to read - and use pread() so you don't have waste time seeking - and because when you read using multiple threads a seek on an open file affects all threads trying to read from the file.
And do not try to read fast into a newly-malloc'd chunk of memory - memset() it first. Because truly fast disk systems can pump data into the CPU faster than the virtual memory manager can create virtual pages for a process.
Consider the following code that loads a dataset of records into a buffer and creates a Record object for each record. A record constitutes one or more columns and this information is uncovered at run-time. However, in this particular example, I have set the number of columns to 3.
typedef unsigned int uint;
typedef struct
{
uint *data;
} Record;
Record *createNewRecord (short num_cols);
int main(int argc, char *argv[])
{
time_t start_time, end_time;
int num_cols = 3;
char *relation;
FILE *stream;
int offset;
char *filename = "file.txt";
stream = fopen(filename, "r");
fseek(stream, 0, SEEK_END);
long fsize = ftell(stream);
fseek(stream, 0, SEEK_SET);
if(!(relation = (char*) malloc(sizeof(char) * (fsize + 1))))
printf((char*)"Could not allocate buffer");
fread(relation, sizeof(char), fsize, stream);
relation[fsize] = '\0';
fclose(stream);
char *start_ptr = relation;
char *end_ptr = (relation + fsize);
while (start_ptr < end_ptr)
{
Record *new_record = createNewRecord(num_cols);
for(short i = 0; i < num_cols; i++)
{
sscanf(start_ptr, " %u %n",
&(new_record->data[i]), &offset);
start_ptr += offset;
}
}
Record *createNewRecord (short num_cols)
{
Record *r;
if(!(r = (Record *) malloc(sizeof(Record))) ||
!(r->data = (uint *) malloc(sizeof(uint) * num_cols)))
{
printf(("Failed to create new a record\n");
}
return r;
}
This code is highly inefficient. My dataset contains around 31 million records (~1 GB) and this code processes only ~200 records per minute. The reason I load the dataset into a buffer is because I'll later have multiple threads process the records in this buffer and hence I want to avoid files accesses. Moreover, I have a 48 GB RAM, so the dataset in memory should not be a problem. Any ideas on how can to speed things up??
SOLUTION: the sscanf function was actually extremely slow and inefficient.. When I switched to strtoul, the job finishes in less than a minute. Malloc-ing ~ 3 million structs of type Record took only few seconds.
Confident that a lurking non-numeric data exist in the file.
int offset;
...
sscanf(start_ptr, " %u %n", &(new_record->data[i]), &offset);
start_ptr += offset;
Notice that if the file begins with non-numeric input, offset is never set and if it had the value of 0, start_ptr += offset; would never increment.
If a non-numeric data exist later in the file like "3x", offset will get the value of 1, and cause the while loop to proceed slowly for it will never get an updated value.
Best to check results of fread(), ftell() and sscanf() for unexpected return values and act accordingly.
Further: long fsizemay be too small a size. Look to using fgetpos() and fsetpos().
Note: to save processing time, consider using strtoul() as it is certainly faster than sscanf(" %u %n"). Again - check for errant results.
BTW: If code needs to uses sscanf(), use sscanf("%u%n"), a tad faster and for your code and the same functionality.
I'm not an optimization professional but I think some tips should help.
First of all, I suggest you use filename and num_cols as macros because they tend to be faster as literals when I don't see you changing their values in code.
Seond, using a struct for storing only one member is generally not recommended, but if you want to use it with functions you should only pass pointers. Since I see you're using malloc to store a struct and again for storing the only member then I suppose that is the reason why it is too slow. You're using twice the memory you need. This might not be the case with some compilers, however. Practically, using a struct with only one member is pointless. If you want to ensure that the integer you get (in your case) is specifically a record, you can typedef it.
You should also make end_pointer and fsize const for some optimization.
Now, as for functionality, have a look at memory mapping io.
This sort code fails for very large input file data because it takes too long for it to finish.
rewind(ptr);
j=0;
while(( fread(&temp,sizeof(temp),1,ptr)==1) &&( j!=lines-1)) //read object by object
{
i=j+1;
while(fread(&temp1,sizeof(temp),1,ptr)==1) //read next object , to compare previous object with next object
{
if(temp.key > temp1.key) //compare key value of object
{
temp2=temp; //if you don't want to change records and just want to change keys use three statements temp2.key =temp.key;
temp=temp1;
temp1=temp2;
fseek(ptr,j*sizeof(temp),0); //move stream to overwrite
fwrite(&temp,sizeof(temp),1,ptr); //you can avoid above swap by changing &temp to &temp1
fseek(ptr,i*sizeof(temp),0); //move stream to overwrite
fwrite(&temp1,sizeof(temp),1,ptr); //you can avoid above swap by changing &temp1 to &temp
}
i++;
}
j++;
fseek(ptr,j*sizeof(temp),0);
}
Any idea on how to make this C code much faster? Also would using qsort() (predefined in C) be much faster and how should be applied to the above code?
You asked the question Sorting based on key from a file and were given various answers about how to sort in memory. You added a supplemental question as an answer, and then created this question instead (which was correct).
Your code here is basically a disk-based bubble sort, with O(N2) complexity, and poor time performance because it is manipulating file buffers and disk. A bubble sort is a bad choice at the best of times — simple, yes, but slow.
The basic ways to speed up sorting programs are:
If possible, read all the data into memory, sort in memory, and write the result out.
If it won't all fit into memory, read as much into memory as possible, sort it, and write the sorted data to a temporary file. Repeat as often as necessary to sort all the data. Then merge the temporary files into one file. If the data set is truly astronomical (or the memory truly minuscule), you may have to create intermediate merge files. These days, though, you have to be sorting many hundreds of gigabytes for that to be an issue at all, even on a 32-bit computer.
Make sure you choose a good sorting algorithm. Quick sort with appropriate pivot selection is very good. You could look up 'introsort' too.
You'll find example in-memory sorting code in the answers to the cross-referenced question (your original question). If you choose to write your own sort, you can consider whether to base the interface on the standard C qsort() function. If you write a Quick Sort, you should look at Quicksort — Choosing the pivot where the answers have copious references.
You'll find example merging code in the answer to Merging multiple sorted files into one file. The merging code out-performs the system sort program in its merge mode, which is intriguing since it is not highly polished code (but it is reasonably workmanlike).
You could look at the external sort program described in Software Tools, though it is a bit esoteric in that it is written in 'RatFor' or Rational Fortran. The design, though, is readily transferrable to other languages.
Yes, by all means, use qsort(). Use it either as SpiderPig suggests by reading the whole file into memory, or as the in-memory sort for runs that do fit into memory preparing for a merge sort. Don't worry about the worst-case performance. A decent implementation will take a the median of (first, last, middle) to get fast sorting for the already-sorted and reverse-order pathological case, plus better average performance in the random case.
This all-in-memory example shows you how to use qsort:
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
typedef struct record_tag
{
int key;
char data[12];
} record_type, *record_ptr;
const record_type * record_cptr;
void create_file(const char *filename, int n)
{
record_type buf;
int i;
FILE *fptr = fopen(filename, "wb");
for (i=0; i<n; ++i)
{
buf.key = rand();
snprintf(buf.data, sizeof buf.data, "%d", buf.key);
fwrite(&buf, sizeof buf, 1, fptr);
}
fclose(fptr);
}
/* Key comparison function used by qsort(): */
int compare_records(const void *x, const void *y)
{
const record_ptr a=(const record_ptr)x;
const record_ptr b=(const record_ptr)y;
return (a->key > b->key) - (a->key < b->key);
}
/* Read an input file of (record_type) records, sort by key field, and write to the output file */
void sort_file(const char *ifname, const char *ofname)
{
const size_t MAXREC = 10000;
int n;
FILE *ifile, *ofile;
record_ptr buffer;
ifile = fopen(ifname, "rb");
buffer = (record_ptr) malloc(MAXREC*sizeof *buffer);
n = fread(buffer, sizeof *buffer, MAXREC, ifile);
fclose(ifile);
qsort(buffer, n, sizeof *buffer, compare_records);
ofile = fopen(ofname, "wb");
fwrite(buffer, sizeof *buffer, n, ofile);
fclose(ofile);
}
void show_file(const char *fname)
{
record_type buf;
int n = 0;
FILE *fptr = fopen(fname, "rb");
while (1 == fread(&buf, sizeof buf, 1, fptr))
{
printf("%9d : %-12s\n", buf.key, buf.data);
++n;
}
printf("%d records read", n);
}
int main(void)
{
srand(time(NULL));
create_file("test.dat", 99);
sort_file("test.dat", "test.out");
show_file("test.out");
return 0;
}
Notice the compare_records function. The qsort() function needs a function that accepts void pointers, so those pointer must be cast to the correct type. Then the pattern:
(left > right) - (left < right)
...will return 1 if the left argument is greater, 0 if they are equal or -1 if the right argument is greater.
The could be improved. First, there is absolutely no error checking. That's not sensible in production code. Second, you could examine the input file to get the file size instead of guessing that it's less than some MAXxxx value. One way to do that is to use ftell. (Follow the link for a file size example.) Then, use that value to allocate a single buffer, just big enough to qsort the data.
If there is not enough room (if the malloc returns NULL) then you can fall back on sorting chunks (with qsort, as in the snippet) that do fit into memory, writing them to separate temporary files, and then merging them into a single output file. That's more complicated, and rarely done since there are sort/merge utility programs designed specifically for sorting large files.
There seems to be a lot of confusion regarding the purpose of the two arguments 'size' and 'count' in fwrite(). I am trying to figure out which will be faster -
fwrite(source, 1, 50000, destination);
or
fwrite(source, 50000, 1, destination);
This is an important decision in my code as this command will be executed millions of times.
Now, I could just jump to testing and use the one which gives better results, but the problem is that the code is intended for MANY platforms.
So,
How can I get a definitive answer to which is better across platforms?
Will implementation logic of fwrite() vary from platform to platform?
I realize there are similar questions (What is the rationale for fread/fwrite taking size and count as arguments?, Performance of fwrite and write size) but do understand that this is a different question regarding the same issue. The answers in similar questions do not suffice in this case.
The performance should not depend on either way, because anyone implementing fwrite would multiply size and count to determine how much I/O to do.
This is exemplified by FreeBSD's libc implementation of fwrite.c, which in its entirety reads (include directives elided):
/*
* Write `count' objects (each size `size') from memory to the given file.
* Return the number of whole objects written.
*/
size_t
fwrite(buf, size, count, fp)
const void * __restrict buf;
size_t size, count;
FILE * __restrict fp;
{
size_t n;
struct __suio uio;
struct __siov iov;
/*
* ANSI and SUSv2 require a return value of 0 if size or count are 0.
*/
if ((count == 0) || (size == 0))
return (0);
/*
* Check for integer overflow. As an optimization, first check that
* at least one of {count, size} is at least 2^16, since if both
* values are less than that, their product can't possible overflow
* (size_t is always at least 32 bits on FreeBSD).
*/
if (((count | size) > 0xFFFF) &&
(count > SIZE_MAX / size)) {
errno = EINVAL;
fp->_flags |= __SERR;
return (0);
}
n = count * size;
iov.iov_base = (void *)buf;
uio.uio_resid = iov.iov_len = n;
uio.uio_iov = &iov;
uio.uio_iovcnt = 1;
FLOCKFILE(fp);
ORIENT(fp, -1);
/*
* The usual case is success (__sfvwrite returns 0);
* skip the divide if this happens, since divides are
* generally slow and since this occurs whenever size==0.
*/
if (__sfvwrite(fp, &uio) != 0)
count = (n - uio.uio_resid) / size;
FUNLOCKFILE(fp);
return (count);
}
The purpose of two arguments gets more clear, if you consider ther return value, which is the count of objects successfuly written/read to/from the stream:
fwrite(src, 1, 50000, dst); // will return 50000
fwrite(src, 50000, 1, dst); // will return 1
The speed might be implementation dependent although, I don't expect any considerable difference.
I'd like to point you to my question, which ended up exposing an interesting performance difference between calling fwrite once and calling fwrite multiple times to write a file "in chunks".
My problem was that there's a bug in Microsoft's implementation of fwrite so files larger than 4GB cannot be written in one call (it hangs at fwrite). So I had to work around this by writing the file in chunks, calling fwrite in a loop until the data was completely written. I found that this latter method always returns faster than the single fwrite call.
I'm in Windows 7 x64 with 32 GB of RAM, which makes write caching pretty aggressive.