I got some doubt about how to allocate shared memory
using some shmget function.what's the syntax exactly ?
also i want to know how to fork child processes in pair which perform different functions individually and then output of processes is given by parent process.
Yeah i under stood your question. Here is the syntax for allocating shared memory for different functions.
thread_identifier = shmget(IPC_PRIVATE, num * sizeof(int), 0777|IPC_CREAT)
And for forking.
if (fork()==0) { printf("desired output "); for(i = 0; i < num; i++) { x[i] = 1 + (rand() % max); printf("%d \n", x[i]); *output *= x[i]; } printf("output %d", *output);
You can use then else loop for entering in another child process.
Related
I have this piece of c/c++ code:
void * myThreadFun(void *vargp)
{
int start = atoi((char*)vargp) % nFracK;
printf("Thread start = %d, dQ = %d\n", start, dQ);
pthread_mutex_lock(&nItermutex);
nIter++;
pthread_mutex_unlock(&nItermutex);
}
void Opt() {
pthread_t thread[200];
char start[100];
for(int i = 0; i < 10; i++) {
sprintf(start, "%d", i);
int ret = pthread_create (&thread[i], NULL, myThreadFun, (void*) start);
printf("ret = %d on thread %d\n", ret, i);
}
for(int i = 0; i < 10; i++)
pthread_join(thread[i], NULL);
}
But it should create 10 threads. I don't understand why, instead, it creates n < 10 threads.
The ret value is always 0 (for 10 times).
But it should create 10 threads. I don't understand why, instead, it creates n < 10 threads. The ret value is always 0 (for 10 times).
Your program contains at least one data race, therefore its behavior is undefined.
The provided source is also is incomplete, so it's impossible to be sure that I can test the same thing you are testing. Nevertheless, I performed the minimum augmentation needed for g++ to compile it without warnings, and tested that:
#include <cstdlib>
#include <cstdio>
#include <pthread.h>
pthread_mutex_t nItermutex = PTHREAD_MUTEX_INITIALIZER;
const int nFracK = 100;
const int dQ = 4;
int nIter = 0;
void * myThreadFun(void *vargp)
{
int start = atoi((char*)vargp) % nFracK;
printf("Thread start = %d, dQ = %d\n", start, dQ);
pthread_mutex_lock(&nItermutex);
nIter++;
pthread_mutex_unlock(&nItermutex);
return NULL;
}
void Opt() {
pthread_t thread[200];
char start[100];
for(int i = 0; i < 10; i++) {
sprintf(start, "%d", i);
int ret = pthread_create (&thread[i], NULL, myThreadFun, (void*) start);
printf("ret = %d on thread %d\n", ret, i);
}
for(int i = 0; i < 10; i++)
pthread_join(thread[i], NULL);
}
int main(void) {
Opt();
return 0;
}
The fact that its behavior is undefined notwithstanding, when I run this program on my Linux machine, it invariably prints exactly ten "Thread start" lines, albeit not all with distinct numbers. The most plausible conclusion is that the program indeed does start ten (additional) threads, which is consistent with the fact that the output also seems to indicate that each call to pthread_create() indicates success by returning 0. I therefore reject your assertion that fewer than ten threads are actually started.
Presumably, the followup question would be why the program does not print the expected output, and here we return to the data race and accompanying undefined behavior. The main thread writes a text representation of iteration variable i into local array data of function Opt, and passes a pointer to that same array to each call to pthread_create(). When it then cycles back to do it again, there is a race between the newly created thread trying to read back the data and the main thread overwriting the array's contents with new data. I suppose that your idea was to avoid passing &i, but this is neither better nor fundamentally different.
You have several options for avoiding a data race in such a situation, prominent among them being:
initialize each thread indirectly from a different object, for example:
int start[10];
for(int i = 0; i < 10; i++) {
start[i] = i;
int ret = pthread_create(&thread[i], NULL, myThreadFun, &start[i]);
}
Note there that each thread is passed a pointer to a different array element, which the main thread does not subsequently modify.
initialize each thread directly from the value passed to it. This is not always a viable alternative, but it is possible in this case:
for(int i = 0; i < 10; i++) {
start[i] = i;
int ret = pthread_create(&thread[i], NULL, myThreadFun,
reinterpret_cast<void *>(static_cast<std::intptr_t>(i)));
}
accompanied by corresponding code in the thread function:
int start = reinterpret_cast<std::intptr_t>(vargp) % nFracK;
This is a fairly common idiom, though more often used when writing in pthreads's native language, C, where it's less verbose.
Use a mutex, semaphore, or other synchronization object to prevent the main thread from modifying the array before the child has read it. (Left as an exercise.)
Any of those options can be used to write a program that produces the expected output, with each thread responsible for printing one line. Supposing, of course, that the expectations of the output do not include that the relative order of the threads' outputs will be the same as the relative order in which they were started. If you want that, then only the option of synchronizing the parent and child threads will achieve it.
It is my first attempt to implement recursion with CUDA. The goal is to extract all the combinations from a set of chars "12345" using the power of CUDA to parallelize dynamically the task. Here is my kernel:
__device__ char route[31] = { "_________________________"};
__device__ char init[6] = { "12345" };
__global__ void Recursive(int depth) {
// up to depth 6
if (depth == 5) return;
// newroute = route - idx
int x = depth * 6;
printf("%s\n", route);
int o = 0;
int newlen = 0;
for (int i = 0; i<6; ++i)
{
if (i != threadIdx.x)
{
route[i+x-o] = init[i];
newlen++;
}
else
{
o = 1;
}
}
Recursive<<<1,newlen>>>(depth + 1);
}
__global__ void RecursiveCount() {
Recursive <<<1,5>>>(0);
}
The idea is to exclude 1 item (the item corresponding to the threadIdx) in each different thread. In each recursive call, using the variable depth, it works over a different base (variable x) on the route device variable.
I expect the kernel prompts something like:
2345_____________________
1345_____________________
1245_____________________
1234_____________________
2345_345_________________
2345_245_________________
2345_234_________________
2345_345__45_____________
2345_345__35_____________
2345_345__34_____________
..
2345_245__45_____________
..
But it prompts ...
·_____________
·_____________
·_____________
·_____________
·_____________
·2345
·2345
·2345
·2345
...
What I´m doing wrong?
What I´m doing wrong?
I may not articulate every problem with your code, but these items should get you a lot closer.
I recommend providing a complete example. In my view it is basically required by Stack Overflow, see item 1 here, note use of the word "must". Your example is missing any host code, including the original kernel call. It's only a few extra lines of code, why not include it? Sure, in this case, I can deduce what the call must have been, but why not just include it? Anyway, based on the output you indicated, it seems fairly evident the launch configuration of the host launch would have to be <<<1,1>>>.
This doesn't seem to be logical to me:
I expect the kernel prompts something like:
2345_____________________
The very first thing your kernel does is print out the route variable, before making any changes to it, so I would expect _____________________. However we can "fix" this by moving the printout to the end of the kernel.
You may be confused about what a __device__ variable is. It is a global variable, and there is only one copy of it. Therefore, when you modify it in your kernel code, every thread, in every kernel, is attempting to modify the same global variable, at the same time. That cannot possibly have orderly results, in any thread-parallel environment. I chose to "fix" this by making a local copy for each thread to work on.
You have an off-by-1 error, as well as an extent error in this loop:
for (int i = 0; i<6; ++i)
The off-by-1 error is due to the fact that you are iterating over 6 possible items (that is, i can reach a value of 5) but there are only 5 items in your init variable (the 6th item being a null terminator. The correct indexing starts out over 0-4 (with one of those being skipped). On subsequent iteration depths, its necessary to reduce this indexing extent by 1. Note that I've chosen to fix the first error here by increasing the length of init. There are other ways to fix, of course. My method inserts an extra _ between depths in the result.
You assume that at each iteration depth, the correct choice of items is the same, and in the same order, i.e. init. However this is not the case. At each depth, the choices of items must be selected not from the unchanging init variable, but from the choices passed from previous depth. Therefore we need a local, per-thread copy of init also.
A few other comments about CUDA Dynamic Parallelism (CDP). When passing pointers to data from one kernel scope to a child scope, local space pointers cannot be used. Therefore I allocate for the local copy of route from the heap, so it can be passed to child kernels. init can be deduced from route, so we can use an ordinary local variable for myinit.
You're going to quickly hit some dynamic parallelism (and perhaps memory) limits here if you continue this. I believe the total number of kernel launches for this is 5^5, which is 3125 (I'm doing this quickly, I may be mistaken). CDP has a pending launch limit of 2000 kernels by default. We're not hitting this here according to what I see, but you'll run into that sooner or later if you increase the depth or width of this operation. Furthermore, in-kernel allocations from the device heap are by default limited to 8KB. I don't seem to be hitting that limit, but probably I am, so my design should probably be modified to fix that.
Finally, in-kernel printf output is limited to the size of a particular buffer. If this technique is not already hitting that limit, it will soon if you increase the width or depth.
Here is a worked example, attempting to address the various items above. I'm not claiming it is defect free, but I think the output is closer to your expectations. Note that due to character limits on SO answers, I've truncated/excerpted some of the output.
$ cat t1639.cu
#include <stdio.h>
__device__ char route[31] = { "_________________________"};
__device__ char init[7] = { "12345_" };
__global__ void Recursive(int depth, const char *oroute) {
char *nroute = (char *)malloc(31);
char myinit[7];
if (depth == 0) memcpy(myinit, init, 6);
else memcpy(myinit, oroute+(depth-1)*6, 6);
myinit[6] = 0;
if (nroute == NULL) {printf("oops\n"); return;}
memcpy(nroute, oroute, 30);
nroute[30] = 0;
// up to depth 6
if (depth == 5) return;
// newroute = route - idx
int x = depth * 6;
//printf("%s\n", nroute);
int o = 0;
int newlen = 0;
for (int i = 0; i<(6-depth); ++i)
{
if (i != threadIdx.x)
{
nroute[i+x-o] = myinit[i];
newlen++;
}
else
{
o = 1;
}
}
printf("%s\n", nroute);
Recursive<<<1,newlen>>>(depth + 1, nroute);
}
__global__ void RecursiveCount() {
Recursive <<<1,5>>>(0, route);
}
int main(){
RecursiveCount<<<1,1>>>();
cudaDeviceSynchronize();
}
$ nvcc -o t1639 t1639.cu -rdc=true -lcudadevrt -arch=sm_70
$ cuda-memcheck ./t1639
========= CUDA-MEMCHECK
2345_____________________
1345_____________________
1245_____________________
1235_____________________
1234_____________________
2345__345________________
2345__245________________
2345__235________________
2345__234________________
2345__2345_______________
2345__345___45___________
2345__345___35___________
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2345__345___345__________
2345__345___45____5______
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2345__345___45____5______
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2345__345___45____4______
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2345__345___45____45____5
2345__345___45____45____4
2345__345___35____5______
2345__345___35____3______
2345__345___35____35_____
2345__345___35____5______
2345__345___35____5_____5
2345__345___35____3______
2345__345___35____3_____3
2345__345___35____35____5
2345__345___35____35____3
2345__345___34____4______
2345__345___34____3______
2345__345___34____34_____
2345__345___34____4______
2345__345___34____4_____4
2345__345___34____3______
2345__345___34____3_____3
2345__345___34____34____4
2345__345___34____34____3
2345__345___345___45_____
2345__345___345___35_____
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2345__345___345___45____5
2345__345___345___45____4
2345__345___345___35____5
2345__345___345___35____3
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2345__345___345___34____3
2345__245___45___________
2345__245___25___________
2345__245___24___________
2345__245___245__________
2345__245___45____5______
2345__245___45____4______
2345__245___45____45_____
2345__245___45____5______
2345__245___45____5_____5
2345__245___45____4______
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2345__245___45____45____5
2345__245___45____45____4
2345__245___25____5______
2345__245___25____2______
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2345__245___25____5______
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2345__245___25____2______
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2345__245___25____25____5
2345__245___25____25____2
2345__245___24____4______
2345__245___24____2______
2345__245___24____24_____
2345__245___24____4______
2345__245___24____4_____4
2345__245___24____2______
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2345__245___24____24____4
2345__245___24____24____2
2345__245___245___45_____
2345__245___245___25_____
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2345__245___245___45____5
2345__245___245___45____4
2345__245___245___25____5
2345__245___245___25____2
2345__245___245___24____4
2345__245___245___24____2
2345__235___35___________
2345__235___25___________
2345__235___23___________
2345__235___235__________
2345__235___35____5______
2345__235___35____3______
2345__235___35____35_____
2345__235___35____5______
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2345__235___35____3______
2345__235___35____3_____3
2345__235___35____35____5
2345__235___35____35____3
2345__235___25____5______
2345__235___25____2______
2345__235___25____25_____
2345__235___25____5______
2345__235___25____5_____5
2345__235___25____2______
2345__235___25____2_____2
2345__235___25____25____5
2345__235___25____25____2
2345__235___23____3______
2345__235___23____2______
2345__235___23____23_____
2345__235___23____3______
2345__235___23____3_____3
2345__235___23____2______
2345__235___23____2_____2
2345__235___23____23____3
2345__235___23____23____2
2345__235___235___35_____
2345__235___235___25_____
2345__235___235___23_____
2345__235___235___35____5
2345__235___235___35____3
2345__235___235___25____5
2345__235___235___25____2
2345__235___235___23____3
2345__235___235___23____2
2345__234___34___________
2345__234___24___________
2345__234___23___________
2345__234___234__________
2345__234___34____4______
2345__234___34____3______
2345__234___34____34_____
2345__234___34____4______
2345__234___34____4_____4
2345__234___34____3______
2345__234___34____3_____3
2345__234___34____34____4
2345__234___34____34____3
2345__234___24____4______
2345__234___24____2______
2345__234___24____24_____
2345__234___24____4______
2345__234___24____4_____4
2345__234___24____2______
2345__234___24____2_____2
2345__234___24____24____4
2345__234___24____24____2
2345__234___23____3______
2345__234___23____2______
2345__234___23____23_____
2345__234___23____3______
2345__234___23____3_____3
2345__234___23____2______
2345__234___23____2_____2
2345__234___23____23____3
2345__234___23____23____2
2345__234___234___34_____
2345__234___234___24_____
2345__234___234___23_____
2345__234___234___34____4
2345__234___234___34____3
2345__234___234___24____4
2345__234___234___24____2
2345__234___234___23____3
2345__234___234___23____2
2345__2345__345__________
2345__2345__245__________
2345__2345__235__________
2345__2345__234__________
2345__2345__345___45_____
2345__2345__345___35_____
2345__2345__345___34_____
2345__2345__345___45____5
2345__2345__345___45____4
2345__2345__345___35____5
2345__2345__345___35____3
2345__2345__345___34____4
2345__2345__345___34____3
2345__2345__245___45_____
2345__2345__245___25_____
2345__2345__245___24_____
2345__2345__245___45____5
2345__2345__245___45____4
2345__2345__245___25____5
2345__2345__245___25____2
2345__2345__245___24____4
2345__2345__245___24____2
2345__2345__235___35_____
2345__2345__235___25_____
2345__2345__235___23_____
2345__2345__235___35____5
2345__2345__235___35____3
2345__2345__235___25____5
2345__2345__235___25____2
2345__2345__235___23____3
2345__2345__235___23____2
2345__2345__234___34_____
2345__2345__234___24_____
2345__2345__234___23_____
2345__2345__234___34____4
2345__2345__234___34____3
2345__2345__234___24____4
2345__2345__234___24____2
2345__2345__234___23____3
2345__2345__234___23____2
1345__345________________
1345__145________________
1345__135________________
1345__134________________
1345__1345_______________
1345__345___45___________
1345__345___35___________
1345__345___34___________
1345__345___345__________
1345__345___45____5______
1345__345___45____4______
1345__345___45____45_____
1345__345___45____5______
1345__345___45____5_____5
1345__345___45____4______
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1345__345___45____45____5
1345__345___45____45____4
1345__345___35____5______
1345__345___35____3______
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1345__345___35____3______
1345__345___35____3_____3
1345__345___35____35____5
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1345__345___34____4______
1345__345___34____3______
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1345__345___34____4______
1345__345___34____4_____4
1345__345___34____3______
1345__345___34____3_____3
1345__345___34____34____4
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1345__345___345___45_____
1345__345___345___35_____
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1345__345___345___45____5
1345__345___345___45____4
1345__345___345___35____5
1345__345___345___35____3
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1345__345___345___34____3
1345__145___45___________
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1345__145___14___________
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1345__145___15____1______
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1345__1345__345__________
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1345__1345__135__________
1345__1345__134__________
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1345__1345__145___15____5
1345__1345__145___15____1
1345__1345__145___14____4
1345__1345__145___14____1
1345__1345__135___35_____
1345__1345__135___15_____
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========= ERROR SUMMARY: 0 errors
$
The answer given by Robert Crovella is correct at the 5th point, the mistake was in the using of init in every recursive call, but I want to clarify something that can be useful for other beginners with CUDA.
I used this variable because when I tried to launch a child kernel passing a local variable I always got the exception: Error: a pointer to local memory cannot be passed to a launch as an argument.
As I´m C# expert developer I´m not used to using pointers (Ref does the low-level-work for that) so I thought there was no way to do it in CUDA/c programming.
As Robert shows in its code it is possible copying the pointer with memalloc for using it as a referable argument.
Here is a kernel simplified as an example of deep recursion.
__device__ char init[6] = { "12345" };
__global__ void Recursive(int depth, const char* route) {
// up to depth 6
if (depth == 5) return;
//declaration for a referable argument (point 6)
char* newroute = (char*)malloc(6);
memcpy(newroute, route, 5);
int o = 0;
int newlen = 0;
for (int i = 0; i < (6 - depth); ++i)
{
if (i != threadIdx.x)
{
newroute[i - o] = route[i];
newlen++;
}
else
{
o = 1;
}
}
printf("%s\n", newroute);
Recursive <<<1, newlen>>>(depth + 1, newroute);
}
__global__ void RecursiveCount() {
Recursive <<<1, 5>>>(0, init);
}
I don't add the main call because I´m using ManagedCUDA for C# but as Robert says it can be figured-out how the call RecursiveCount is.
About ending arrays of char with /0 ... sorry but I don't know exactly what is the benefit; this code works fine without them.
Originally the problem appeared when I tried to optimize an algorithm for neon arm and some minor part of it was taking 80% of according to profiler. I tried to test to see what can be done to improve it and for that I created array of function pointers to different versions of my optimized function and then I run them in the loop to see in profiler which one performs better:
typedef unsigned(*CalcMaxFunc)(const uint16_t a[8][4], const uint16_t b[4][4]);
CalcMaxFunc CalcMaxFuncs[] =
{
CalcMaxFunc_NEON_0,
CalcMaxFunc_NEON_1,
CalcMaxFunc_NEON_2,
CalcMaxFunc_NEON_3,
CalcMaxFunc_C_0
};
int N = sizeof(CalcMaxFunc) / sizeof(CalcMaxFunc[0]);
for (int i = 0; i < 10 * N; ++i)
{
auto f = CalcMaxFunc[i % N];
unsigned retI = f(a, b);
// just random code to ensure that cpu waits for the results
// and compiler doesn't optimize it away
if (retI > 1000000)
break;
ret |= retI;
}
I got surprising results: performance of a function was totally depend on its position within CalcMaxFuncs array. For example, when I swapped CalcMaxFunc_NEON_3 to be first it would be 3-4 times slower and according to profiler it would stall at the last bit of the function where it tried to move data from neon to arm register.
So, what does it make stall sometimes and not in other times? BY the way, I profile on iPhone6 in xcode if that matters.
When I intentionally introduced neon pipeline stalls by mixing-in some floating point division between calling these functions in the loop I eliminated unreliable behavior, now all of them perform the same regardless of the order in which they were called. So, why in the first place did I have that problem and what can I do to eliminate it in actual code?
Update:
I tried to create a simple test function and then optimize it in stages and see how I could possibly avoid neon->arm stalls.
Here's the test runner function:
void NeonStallTest()
{
int findMinErr(uint8_t* var1, uint8_t* var2, int size);
srand(0);
uint8_t var1[1280];
uint8_t var2[1280];
for (int i = 0; i < sizeof(var1); ++i)
{
var1[i] = rand();
var2[i] = rand();
}
#if 0 // early exit?
for (int i = 0; i < 16; ++i)
var1[i] = var2[i];
#endif
int ret = 0;
for (int i=0; i<10000000; ++i)
ret += findMinErr(var1, var2, sizeof(var1));
exit(ret);
}
And findMinErr is this:
int findMinErr(uint8_t* var1, uint8_t* var2, int size)
{
int ret = 0;
int ret_err = INT_MAX;
for (int i = 0; i < size / 16; ++i, var1 += 16, var2 += 16)
{
int err = 0;
for (int j = 0; j < 16; ++j)
{
int x = var1[j] - var2[j];
err += x * x;
}
if (ret_err > err)
{
ret_err = err;
ret = i;
}
}
return ret;
}
Basically it it does sum of squared difference between each uint8_t[16] block and returns index of the block pair that has lowest squared difference. So, then I rewrote it in neon intrisics (no particular attempt was made to make it fast, as it's not the point):
int findMinErr_NEON(uint8_t* var1, uint8_t* var2, int size)
{
int ret = 0;
int ret_err = INT_MAX;
for (int i = 0; i < size / 16; ++i, var1 += 16, var2 += 16)
{
int err;
uint8x8_t var1_0 = vld1_u8(var1 + 0);
uint8x8_t var1_1 = vld1_u8(var1 + 8);
uint8x8_t var2_0 = vld1_u8(var2 + 0);
uint8x8_t var2_1 = vld1_u8(var2 + 8);
int16x8_t s0 = vreinterpretq_s16_u16(vsubl_u8(var1_0, var2_0));
int16x8_t s1 = vreinterpretq_s16_u16(vsubl_u8(var1_1, var2_1));
uint16x8_t u0 = vreinterpretq_u16_s16(vmulq_s16(s0, s0));
uint16x8_t u1 = vreinterpretq_u16_s16(vmulq_s16(s1, s1));
#ifdef __aarch64__1
err = vaddlvq_u16(u0) + vaddlvq_u16(u1);
#else
uint32x4_t err0 = vpaddlq_u16(u0);
uint32x4_t err1 = vpaddlq_u16(u1);
err0 = vaddq_u32(err0, err1);
uint32x2_t err00 = vpadd_u32(vget_low_u32(err0), vget_high_u32(err0));
err00 = vpadd_u32(err00, err00);
err = vget_lane_u32(err00, 0);
#endif
if (ret_err > err)
{
ret_err = err;
ret = i;
#if 0 // enable early exit?
if (ret_err == 0)
break;
#endif
}
}
return ret;
}
Now, if (ret_err > err) is clearly data hazard. Then I manually "unrolled" loop by two and modified code to use err0 and err1 and check them after performing next round of compute. According to profiler I got some improvements. In simple neon loop I got roughly 30% of entire function spent in the two lines vget_lane_u32 followed by if (ret_err > err). After I unrolled by two these operations started to take 25% (e.g. I got roughly 10% overall speedup). Also, check armv7 version, there is only 8 instructions between when err0 is set (vmov.32 r6, d16[0]) and when it's accessed (cmp r12, r6). T
Note, in the code early exit is ifdefed out. Enabling it would make function even slower. If I unrolled it by four and changed to use four errN variables and deffer check by two rounds then I still saw vget_lane_u32 in profiler taking too much time. When I checked generated asm, appears that compiler destroys all the optimizations attempts because it reuses some of the errN registers which effectively makes CPU access results of vget_lane_u32 much earlier than I want (and I aim to delay access by 10-20 instructions). Only when I unrolled by 4 and marked all four errN as volatile vget_lane_u32 totally disappeared from the radar in profiler, however, the if (ret_err > errN) check obviously got slow as hell as now these probably ended up as regular stack variables overall these 4 checks in 4x manual loop unroll started to take 40%. Looks like with proper manual asm it's possible to make it work properly: have early loop exit, while avoiding neon->arm stalls and have some arm logic in the loop, however, extra maintenance required to deal with arm asm makes it 10x more complex to maintain that kind of code in a large project (that doesn't have any armasm).
Update:
Here's sample stall when moving data from neon to arm register. To implement early exist I need to move from neon to arm once per loop. This move alone takes more than 50% of entire function according to sampling profiler that comes with xcode. I tried to add lots of noops before and/or after the mov, but nothing seems to affect results in profiler. I tried to use vorr d0,d0,d0 for noops: no difference. What's the reason for the stall, or the profiler simply shows wrong results?
I have the following code which progressively goes through a string of bits and rearrange them into blocks of 20bytes. I'm using 32*8 blocks with 40 threads per block. However the process takes something like 36ms on my GT630M. Are there any further optimization I can do? Especially with regard to removing the if-else in the inner most loop.
__global__ void test(unsigned char *data)
{
__shared__ unsigned char dataBlock[20];
__shared__ int count;
count = 0;
unsigned char temp = 0x00;
for(count=0; count<(streamSize/8); count++)
{
for(int i=0; i<8; i++)
{
if(blockIdx.y >= i)
temp |= (*(data + threadIdx.x*(blockIdx.x + gridDim.x*(i+count)))&(0x01<<blockIdx.y))>>(blockIdx.y - i);
else
temp |= (*(data + threadIdx.x*(blockIdx.x + gridDim.x*(i+count)))&(0x01<<blockIdx.y))<<(i - blockIdx.y);
}
dataBlock[threadIdx.x] = temp;
//do something
}
}
It's not clear what your code is trying to accomplish, but a couple obvious opportunities are:
1) if possible, use 32-bit words instead of unsigned char.
2) use block sizes that are multiples of 32.
3) The conditional code may not be costing you as much as you expect. You can check by compiling with --cubin --gpu-architecture sm_xx (where xx is the SM version of your target hardware), and using cuobjdump --dump-sass on the resulting cubin file to look at the generated assembly. You may have to modify the source code to loft the common subexpression into a separate variable, and/or use the ternary operator ? : to hint to the compiler to use predication.
for (int i = 2; i * i <= n; i++)
for (int i = 2; i <= SQRT(n); i++)
just wondering which is faster I looked at some primitive algorithms for getting roots and it would seem to me that squaring the number would be faster but I don't know for sure. These loops are for determining a numbers "primeness".
Shouldn't the comaprison be between
int sqrt = SQRT(n);
for (int i = 2; i <= sqrt; i++)
and
for (int i = 2; i * i <= n; i++)
The answer will depend on how many loop iterations you do. The sqrt method does less work per iteration, but it has a higher start-up cost. Mind you, this reeks of premature optimisation.
Compiler may 'cache' result of SQRT (n), but i * i it should compute on each step.
Square root will take longer, unless it's implemented in hardware, lookup, or a special machine code version. Newton iteration is the algorithm of choice; it converges quadratically.
Best to benchmark for yourself. I'd recommend moving the call to square root outside the loop so you only do it once rather than every time you check the exit condition.
Why not skip both of them and use some clever maths? The Following code avoid both of them using the Property that Sum of the First n odd numbers is always a perfect square.
A shameless plug for my old blogpost (from my dead blog)
int isPrime(int n)
{
int squares = 1;
int odd = 3;
if( ((n & 1) == 0) || (n < 9)) return (n == 2) || ((n > 1) && (n & 1));
else
{
for( ;squares <= n; odd += 2)
{
if( n % odd == 0)
return 0;
squares+=odd;
}
return 1;
}
}
The square will be faster.
But the square will overflow if n is larger than the square root of the largest int, and then the comparison will go wrong. The square root function could (and you would expect to) be implemented in such a way that is can be calculated on arguments all the way up to the largest representable int. That means it won't go wrong in that way.
In Java, the largest int is 2^31 - 1, which means its square root is just under 46341. If you want to look for primes larger than that, the squaring would stop you.