I am trying to understand some aspects of the MPI.
During the creation of the program, which is to measure latency between send/recv of two processes, I was faced with strange effects.
I tried to measure the result of many iterations, and received a response that matches the other benchmarks. Then I decided to display values after each iteration and was surprised: they ranged between four values that have not changed. I also drew attention to some very high values.
The code that calculates the value of latency and sample values is below:
int main()
{
MPI::Init();
Proc_Rank = MPI::COMM_WORLD.Get_rank();
for(int i = 0; i < 100; ++i)
latency_test(Proc_Rank, 1, 0);
MPI::Finalize();
return 0;
}
void latency_test(int Proc_Rank, int Iterations_Num, int Size)
{
double Total_Time, Latency;
double t1, t2;
char *Send_Buffer = new char[Size];
char *Recv_Buffer = new char[Size];
for(int i = 0; i < Size; i++){
Send_Buffer[i] = 'a';
}
for(int i = 0; i < Size; i++){
Recv_Buffer[i] = 'b';
}
MPI::COMM_WORLD.Barrier();
t1 = MPI::Wtime();
for(int i = 0; i < Iterations_Num; i++){
if (Proc_Rank == 0){
MPI::COMM_WORLD.Send(Send_Buffer, Size, MPI::CHAR, 1, 0);
MPI::COMM_WORLD.Recv(Recv_Buffer,Size,MPI::CHAR,1,
MPI::ANY_TAG);
}
else if (Proc_Rank==1) { MPI::COMM_WORLD.Recv(Recv_Buffer,Size,MPI::CHAR,0,MPI::ANY_TAG);
MPI::COMM_WORLD.Send(Send_Buffer, Size, MPI::CHAR, 0, 0);
}
}
t2 = MPI::Wtime();
delete []Send_Buffer;
delete []Recv_Buffer;
Total_Time = t2-t1;
if(Proc_Rank == 0){
Latency = (Total_Time / (Iterations_Num * 2.0)) * 1000000.0;
printf("%10.10f\n", Latency);
}
}
Part of the result:
5.4836273193
1.0728836060
0.9536743164
1.0728836060
0.4768371582
0.9536743164
0.5960464478
6.5565109253
0.9536743164
0.9536743164
1.0728836060
0.5960464478
0.4768371582
0.4768371582
Why are 4 fixed values randomly repeat? And why there are rare very large values?
As pointed out by Zulan, the resolution of the timer used by MPI_Wtime is not infinite. You can query the timer resolution by calling MPI_Wtick (MPI::Wtick in the C++ bindings). Measuring a single ping-pong round that lasts less than a microsecond is prone to very high statistical uncertainty, especially since the OS jitter, which is the random delay of the process execution due to other OS activities or processes being scheduled on the same CPU, could be several microseconds. No respectable MPI benchmark would do a single ping-pong round with empty messages.
As a side note, you are using a wildcard receive (MPI_ANY_TAG) in one of the processes. Those tend to be slower than fully-specified receives, especially when it comes to network equipment.
Related
I am new in using OpenMP.
I think that use max reduction clause to find the max element of an array is not such a bad idea, but in fact the parallel for loop ran much slower than serial one.
int main() {
double sta, end, elapse_t;
int bsize = 46000;
int q = bsize;
int max_val = 0;
double *buffer;
buffer = (double*)malloc(bsize*sizeof(double));
srand(time(NULL));
for(int i=0;i<q;i++)
buffer[i] = rand()%10000;
sta = omp_get_wtime();
//int i;
#pragma omp parallel for reduction(max : max_val)
for(int i=0;i<q; i++)
{
max_val = max_val > buffer[i] ? max_val : buffer[i];
}
end = omp_get_wtime();
printf("parallel maximum time %f\n", end-sta);
sta = omp_get_wtime();
for(int i=0;i<q; i++)
{
max_val = max_val > buffer[i] ? max_val : buffer[i];
}
end = omp_get_wtime();
printf("serial maximum time %f\n", end-sta);
free(buffer);
return 0;}
Compile command
gcc-7 kp_omp.cpp -o kp_omp -fopenmp
Execution results
./kp_omp
parallel maximum time 0.000505
serial maximum time 0.000266
As for the CPU, it is an Intel Core i7-6700 with 8 cores.
Whenever you parallelise a loop openMP needs to perform some operations, for example creating the threads. Those operations result in some overhead and this in turns implies that, for each loop, there is a minimum number of iterations under which it is not convenient to parallelise.
If I execute your code I obtain the same results you have:
./kp_omp
parallel maximum time 0.000570
serial maximum time 0.000253
However if I modify bsize in line 8 such that
int bsize = 100000;
I obtain
./kp_omp
parallel maximum time 0.000323
serial maximum time 0.000552
So the parallelised version got faster than the sequential. Part of the challenges you encounter when you try to speedup the execution of a code is to understand when it is convenient to parallelise and when the overhead of the parallelisation would kill your expected gain in performance.
I've got a strange performance inversion on filter kernel with and without branching. Kernel with branching runs ~1.5x faster than the kernel without branching.
Basically I need to sort a bunch of radiance rays then apply interaction kernels. Since there are a lot of accompanying data, I can't use something like thrust::sort_by_key() many times.
Idea of the algorithm:
Run a loop for all possible interaction types (which is five)
At every cycle a warp thread votes for its interaction type
After loop completion every warp thread knows about another threads with the same interaction type
Threads elect they leader (per interaction type)
Leader updates interactions offsets table using atomicAdd
Each thread writes its data to corresponding offset
I used techniques described in this Nvidia post https://devblogs.nvidia.com/parallelforall/cuda-pro-tip-optimized-filtering-warp-aggregated-atomics/
My first kernel contains a branch inside loop and runs for ~5ms:
int active;
int leader;
int warp_progress;
for (int i = 0; i != hit_interaction_count; ++i)
{
if (i == decision)
{
active = __ballot(1);
leader = __ffs(active) - 1;
warp_progress = __popc(active);
}
}
My second kernel use lookup table of two elements, use no branching and runs for ~8ms:
int active = 0;
for (int i = 0; i != hit_interaction_count; ++i)
{
const int masks[2] = { 0, ~0 };
int mask = masks[i == decision];
active |= (mask & __ballot(mask));
}
int leader = __ffs(active) - 1;
int warp_progress = __popc(active);
Common part:
int warp_offset;
if (lane_id() == leader)
warp_offset = atomicAdd(&interactions_offsets[decision], warp_progress);
warp_offset = warp_broadcast(warp_offset, leader);
...copy data here...
How can that be? Is there any way to implement such filter kernel so it will run faster than branching one?
UPD: Complete source code can be found in filter_kernel cuda_equation/radiance_cuda.cu at https://bitbucket.org/radiosity/engine/src
I think this is CPU programmer brain deformation. On CPU I expect performance boost because of eliminated branch and branch misprediction penalty.
But there is no branch prediction on GPU and no penalty, so only instructions count matters.
First I need to rewrite code to the simple one.
With branch:
int active;
for (int i = 0; i != hit_interaction_count; ++i)
if (i == decision)
active = __ballot(1);
Without branch:
int active = 0;
for (int i = 0; i != hit_interaction_count; ++i)
{
int mask = 0 - (i == decision);
active |= (mask & __ballot(mask));
}
In first version there are ~3 operations: compare, if and __ballot().
In second version there are ~5 operations: compare, make mask, __ballot(), & and |=.
And there are ~15 ops in common code.
Both loops runs for 5 cycles. It total 35 ops in first, and 45 ops in second. This calculation can explain performance degradation.
struct xnode
{
float *mat;
};
void testScaling( )
{
int N = 1000000; ///total num matrices
int dim = 10;
//memory for matrices
std::vector<xnode> nodeArray(N);
for( int k = 0; k < N; ++k )
nodeArray[k].mat = new float [dim*dim];
//memory for Y
std::vector<float*> Y(N,0);
for( int k = 0; k < N; ++k )
Y[k] = new float [dim];
//shared X
float* X = new float [dim];
for(int i = 0; i < dim; ++i ) X[i] = 1.0;
//init mats
for( int k = 0; k < N; ++k )
{
for( int i=0; i<dim*dim; ++i )
nodeArray[k].mat[i] = 0.25+((float)i)/3;
}
int NTIMES = 500;
//gemv args
char trans = 'N';
int lda = dim;
int incx = 1;
float alpha =1 , beta = 0;
//threads
int thr[4];
thr[0] =1 ; thr[1] = 2; thr[2] = 4; thr[3] = 8;
for( int t = 0; t<4; ++t )//test for nthreads
{
int nthreads = thr[t];
double t_1 = omp_get_wtime();
for( int ii = 0; ii < NTIMES; ++ii )//do matvec NTIMES
{
#pragma omp parallel for num_threads(nthreads)
for( int k=0; k<N; ++k )
{
//compute Y[k] = mat[k] * X;
GEMV(&trans, &dim, &dim, &alpha, nodeArray[k].mat, &lda, X, &incx, &beta, Y[k], &incx);
//GEMV(&trans, &dim, &dim, &alpha, nodeArray[0].mat, &lda, X, &incx, &beta, Y[k], &incx);
}
}
double t_2 = omp_get_wtime();
std::cout << "Threads " << nthreads << " time " << (t_2-t_1)/NTIMES << std::endl;
}
//clear memory
for( int k = 0; k < N; ++k )
{
delete [] nodeArray[k].mat;
delete [] Y[k];
}
delete [] X;
}
The above code parallelizes the matrix-vector product of N matrices of size dim, and stores results in N output vectors. The average of 500 products is taken as the time per matrix-vector product. The matrix-vector products in the above example are all of equal size and thus the threads should be perfectly balanced - we should achieve a performance scaling close to ideal 8x. The following are the observations (Machine – Intel Xeon 3.1Ghz.2 processors,8cores each, HyperThreading enabled, Windows, VS2012, Intel MKL, Intel OMP library).
OBSERVATION 1:
dim=10 N=1000000
Threads 1 - time 0.138068s
Threads 2 - time 0.0729147s
Threads 4 - time 0.0360527s
Threads 8 - time 0.0224268s (6.1x on 8threads)
OBSERVATION 2 :
dim=20 N=1000000
Threads 1 time 0.326617
Threads 2 time 0.185706
Threads 4 time 0.0886508
Threads 8 time 0.0733666 (4.5x on 8 threads).
Note – I ran VTune on this case. It showed CPUTime 267.8sec, Overhead time 43 sec, Spin time – 8 sec. The overhead time is all spent in a libiomp function (intel library). 8Threads/1Thread scaling is poor for such cases.
Next - in the gemv for loop, we change nodeArray[k].mat to nodeArray[0].mat (see commented statement), so that only the first matrix is used for all the matrix-vector products.
OBSERVATION 3
dim=20 N=1000000
Threads 1 time 0.152298 (The serial time is halved)
Threads 2 time 0.0769173
Threads 4 time 0.0384086
Threads 8 time 0.019336 (7.87x on 8 threads)
Thus I get almost ideal scaling - why is this behavior? VTune says that a significant portion of CPU time is spent in synchronization and thread overhead. Here it seems there is no relation between the load balancing and thread synchronization. As matrix size is increased the granularity should increase and thread overhead should be proportionately small. But as we increase from size 10 to 20 the scaling is weakening. When we use nodeArray[0].mat (only the first matrix) for doing all the matrix-vector products the cache is updated only once (since the compiler knows this during optimization) and we get near ideal scaling. Thus the synchronization overhead seems to be related to some cache related issue. I have tried a number of other things like setting KMP_AFFINITY and varying load distribution but that did not buy me anything.
My questions are:
1. I dont have a clear idea about how does the cache performance affect openMP thread synchronization. Can someone explain this?
2. Can anything be done about improving the scaling and reducing the overhead?
Thanks
Summary:
Any ideas about how to further improve upon the basic scatter operation in CUDA? Especially if one knows it will only be used to compact a larger array into a smaller one? or why the below methods of vectorizing memory ops and shared memory didn't work? I feel like there may be something fundamental I am missing and any help would be appreciated.
EDIT 03/09/15: So I found this Parallel For All Blog post "Optimized Filtering with Warp-Aggregated Atomics". I had assumed atomics would be intrinsically slower for this purpose, however I was wrong - especially since I don't think I care about maintaining element order in the array during my simulation. I'll have to think about it some more and then implement it to see what happens!
EDIT 01/04/16: I realized I never wrote about my results. Unfortunately in that Parallel for All Blog post they compared the global atomic method for compact to the Thrust prefix-sum compact method, which is actually quite slow. CUB's Device::IF is much faster than Thrust's - as is the prefix-sum version I wrote using CUB's Device::Scan + custom code. The warp-aggregrate global atomic method is still faster by about 5-10%, but nowhere near the 3-4x faster I had been hoping for based on the results in the blog. I'm still using the prefix-sum method as while maintaining element order is not necessary, I prefer the consistency of the prefix-sum results and the advantage from the atomics is not very big. I still try various methods to improve compact, but so far only marginal improvements (2%) at best for dramatically increased code complexity.
Details:
I am writing a simulation in CUDA where I compact out elements I am no longer interested in simulating every 40-60 time steps. From profiling it seems that the scatter op takes up the most amount of time when compacting - more so than the filter kernel or the prefix sum. Right now I use a pretty basic scatter function:
__global__ void scatter_arrays(float * new_freq, const float * const freq, const int * const flag, const int * const scan_Index, const int freq_Index){
int myID = blockIdx.x*blockDim.x + threadIdx.x;
for(int id = myID; id < freq_Index; id+= blockDim.x*gridDim.x){
if(flag[id]){
new_freq[scan_Index[id]] = freq[id];
}
}
}
freq_Index is the number of elements in the old array. The flag array is the result from the filter. Scan_ID is the result from the prefix sum on the flag array.
Attempts I've made to improve it are to read the flagged frequencies into shared memory first and then write from shared memory to global memory - the idea being that the writes to global memory would be more coalesced amongst the warps (e.g. instead of thread 0 writing to position 0 and thread 128 writing to position 1, thread 0 would write to 0 and thread 1 would write to 1). I also tried vectorizing the reads and the writes - instead of reading and writing floats/ints I read/wrote float4/int4 from the global arrays when possible, so four numbers at a time. This I thought might speed up the scatter by having fewer memory ops transferring larger amounts of memory. The "kitchen sink" code with both vectorized memory loads/stores and shared memory is below:
const int compact_threads = 256;
__global__ void scatter_arrays2(float * new_freq, const float * const freq, const int * const flag, const int * const scan_Index, const int freq_Index){
int gID = blockIdx.x*blockDim.x + threadIdx.x; //global ID
int tID = threadIdx.x; //thread ID within block
__shared__ float row[4*compact_threads];
__shared__ int start_index[1];
__shared__ int end_index[1];
float4 myResult;
int st_index;
int4 myFlag;
int4 index;
for(int id = gID; id < freq_Index/4; id+= blockDim.x*gridDim.x){
if(tID == 0){
index = reinterpret_cast<const int4*>(scan_Index)[id];
myFlag = reinterpret_cast<const int4*>(flag)[id];
start_index[0] = index.x;
st_index = index.x;
myResult = reinterpret_cast<const float4*>(freq)[id];
if(myFlag.x){ row[0] = myResult.x; }
if(myFlag.y){ row[index.y-st_index] = myResult.y; }
if(myFlag.z){ row[index.z-st_index] = myResult.z; }
if(myFlag.w){ row[index.w-st_index] = myResult.w; }
}
__syncthreads();
if(tID > 0){
myFlag = reinterpret_cast<const int4*>(flag)[id];
st_index = start_index[0];
index = reinterpret_cast<const int4*>(scan_Index)[id];
myResult = reinterpret_cast<const float4*>(freq)[id];
if(myFlag.x){ row[index.x-st_index] = myResult.x; }
if(myFlag.y){ row[index.y-st_index] = myResult.y; }
if(myFlag.z){ row[index.z-st_index] = myResult.z; }
if(myFlag.w){ row[index.w-st_index] = myResult.w; }
if(tID == blockDim.x -1 || gID == mutations_Index/4 - 1){ end_index[0] = index.w + myFlag.w; }
}
__syncthreads();
int count = end_index[0] - st_index;
int rem = st_index & 0x3; //equivalent to modulo 4
int offset = 0;
if(rem){ offset = 4 - rem; }
if(tID < offset && tID < count){
new_mutations_freq[population*new_array_Length+st_index+tID] = row[tID];
}
int tempID = 4*tID+offset;
if((tempID+3) < count){
reinterpret_cast<float4*>(new_freq)[tID] = make_float4(row[tempID],row[tempID+1],row[tempID+2],row[tempID+3]);
}
tempID = tID + offset + (count-offset)/4*4;
if(tempID < count){ new_freq[st_index+tempID] = row[tempID]; }
}
int id = gID + freq_Index/4 * 4;
if(id < freq_Index){
if(flag[id]){
new_freq[scan_Index[id]] = freq[id];
}
}
}
Obviously it gets a bit more complicated. :) While the above kernel seems stable when there are hundreds of thousands of elements in the array, I've noticed a race condition when the array numbers in the tens of millions. I'm still trying to track the bug down.
But regardless, neither method (shared memory or vectorization) together or alone improved performance. I was especially surprised by the lack of benefit from vectorizing the memory ops. It had helped in other functions I had written, though now I am wondering if maybe it helped because it increased Instruction-Level-Parallelism in the calculation steps of those other functions rather than the fewer memory ops.
I found the algorithm mentioned in this poster (similar algorithm also discussed in this paper) works pretty well, especially for compacting large arrays. It uses less memory to do it and is slightly faster than my previous method (5-10%). I put in a few tweaks to the poster's algorithm: 1) eliminating the final warp shuffle reduction in phase 1, can simply sum the elements as they are calculated, 2) giving the function the ability to work over more than just arrays sized as a multiple of 1024 + adding grid-strided loops, and 3) allowing each thread to load their registers simultaneously in phase 3 instead of one at a time. I also use CUB instead of Thrust for Inclusive sum for faster scans. There may be more tweaks I can make, but for now this is good.
//kernel phase 1
int myID = blockIdx.x*blockDim.x + threadIdx.x;
//padded_length is nearest multiple of 1024 > true_length
for(int id = myID; id < (padded_length >> 5); id+= blockDim.x*gridDim.x){
int lnID = threadIdx.x % warp_size;
int warpID = id >> 5;
unsigned int mask;
unsigned int cnt=0;//;//
for(int j = 0; j < 32; j++){
int index = (warpID<<10)+(j<<5)+lnID;
bool pred;
if(index > true_length) pred = false;
else pred = predicate(input[index]);
mask = __ballot(pred);
if(lnID == 0) {
flag[(warpID<<5)+j] = mask;
cnt += __popc(mask);
}
}
if(lnID == 0) counter[warpID] = cnt; //store sum
}
//kernel phase 2 -> CUB Inclusive sum transforms counter array to scan_Index array
//kernel phase 3
int myID = blockIdx.x*blockDim.x + threadIdx.x;
for(int id = myID; id < (padded_length >> 5); id+= blockDim.x*gridDim.x){
int lnID = threadIdx.x % warp_size;
int warpID = id >> 5;
unsigned int predmask;
unsigned int cnt;
predmask = flag[(warpID<<5)+lnID];
cnt = __popc(predmask);
//parallel prefix sum
#pragma unroll
for(int offset = 1; offset < 32; offset<<=1){
unsigned int n = __shfl_up(cnt, offset);
if(lnID >= offset) cnt += n;
}
unsigned int global_index = 0;
if(warpID > 0) global_index = scan_Index[warpID - 1];
for(int i = 0; i < 32; i++){
unsigned int mask = __shfl(predmask, i); //broadcast from thread i
unsigned int sub_group_index = 0;
if(i > 0) sub_group_index = __shfl(cnt, i-1);
if(mask & (1 << lnID)){
compacted_array[global_index + sub_group_index + __popc(mask & ((1 << lnID) - 1))] = input[(warpID<<10)+(i<<5)+lnID];
}
}
}
}
EDIT: There is a newer article by a subset of the poster authors where they examine a faster variation of compact than what is written above. However, their new version is not order preserving, so not useful for myself and I haven't implemented it to test it out. That said, if your project doesn't rely on object order, their newer compact version can probably speed up your algorithm.
I'm teaching myself OpenCL by trying to optimize the mpeg4dst reference audio encoder. I achieved a 3x speedup by using vector instructions on CPU but I figured the GPU could probably do better.
I'm focusing on computing auto-correlation vectors in OpenCL as my first area of improvement. The CPU code is:
for (int i = 0; i < NrOfChannels; i++) {
for (int shift = 0; shift <= PredOrder[ChannelFilter[i]]; shift++)
vDSP_dotpr(Signal[i] + shift, 1, Signal[i], 1, &out, NrOfChannelBits - shift);
}
NrOfChannels = 6
PredOrder = 129
NrOfChannelBits = 150528.
On my test file, this function take approximately 188ms to complete.
Here's my OpenCL method:
kernel void calculateAutocorrelation(size_t offset,
global const float *input,
global float *output,
size_t size) {
size_t index = get_global_id(0);
size_t end = size - index;
float sum = 0.0;
for (size_t i = 0; i < end; i++)
sum += input[i + offset] * input[i + offset + index];
output[index] = sum;
}
This is how it is called:
gcl_memcpy(gpu_signal_in, Signal, sizeof(float) * NrOfChannels * MAXCHBITS);
for (int i = 0; i < NrOfChannels; i++) {
size_t sz = PredOrder[ChannelFilter[i]] + 1;
cl_ndrange range = { 1, { 0, 0, 0 }, { sz, 0, 0}, { 0, 0, 0 } };
calculateAutocorrelation_kernel(&range, i * MAXCHBITS, (cl_float *)gpu_signal_in, (cl_float *)gpu_out, NrOfChannelBits);
gcl_memcpy(out, gpu_out, sizeof(float) * sz);
}
According to Instruments, my OpenCL implementation seems to take about 13ms, with about 54ms of memory copy overhead (gcl_memcpy).
When I use a much larger test file, 1 minute of 2-channel music vs, 1 second of 6-channel, while the measured performance of the OpenCL code seems to be the same, the CPU usage falls to about 50% and the whole program takes about 2x longer to run.
I can't find a cause for this in Instruments and I haven't read anything yet that suggests that I should expect very heavy overhead switching in and out of OpenCL.
If I'm reading your kernel code correctly, each work item is iterating over all of the data from it's location to the end. This isn't going to be efficient. For one (and the primary performance concern), the memory accesses won't be coalesced and so won't be at full memory bandwidth. Secondly, because each work item has a different amount of work, there will be branch divergence within a work group, which will leave some threads idle waiting for others.
This seems like it has a lot in common with a reduction problem and I'd suggest reading up on "parallel reduction" to get some hints about doing an operation like this in parallel.
To see how memory is being read, work out how 16 work items (say, global_id 0 to 15) will be reading data for each step.
Note that if every work item in a work group access the same memory, there is a "broadcast" optimization the hardware can make. So just reversing the order of your loop could improve things.