问题
I found the method 'vectorized/batch sort' and 'nested sort' on below link. How to use Thrust to sort the rows of a matrix?
When I tried this method for 500 row and 1000 elements, the result of them are
- vectorized/batch sort : 66ms
- nested sort : 3290ms
I am using 1080ti HOF model to do this operation but it takes too long compared to your case.
But in the below link, it could be less than 10ms and almost 100 microseconds.
(How to find median value in 2d array for each column with CUDA?)
Could you recommend how to optimize this method to reduce operation time?
#include <thrust/device_vector.h>
#include <thrust/device_ptr.h>
#include <thrust/host_vector.h>
#include <thrust/sort.h>
#include <thrust/execution_policy.h>
#include <thrust/generate.h>
#include <thrust/equal.h>
#include <thrust/sequence.h>
#include <thrust/for_each.h>
#include <iostream>
#include <stdlib.h>
#define NSORTS 500
#define DSIZE 1000
int my_mod_start = 0;
int my_mod() {
return (my_mod_start++) / DSIZE;
}
bool validate(thrust::device_vector<int> &d1, thrust::device_vector<int> &d2) {
return thrust::equal(d1.begin(), d1.end(), d2.begin());
}
struct sort_functor
{
thrust::device_ptr<int> data;
int dsize;
__host__ __device__
void operator()(int start_idx)
{
thrust::sort(thrust::device, data + (dsize*start_idx), data + (dsize*(start_idx + 1)));
}
};
#include <time.h>
#include <windows.h>
unsigned long long dtime_usec(LONG start) {
SYSTEMTIME timer2;
GetSystemTime(&timer2);
LONG end = (timer2.wSecond * 1000) + timer2.wMilliseconds;
return (end-start);
}
int main() {
for (int i = 0; i < 3; i++) {
SYSTEMTIME timer1;
cudaDeviceSetLimit(cudaLimitMallocHeapSize, (16 * DSIZE*NSORTS));
thrust::host_vector<int> h_data(DSIZE*NSORTS);
thrust::generate(h_data.begin(), h_data.end(), rand);
thrust::device_vector<int> d_data = h_data;
// first time a loop
thrust::device_vector<int> d_result1 = d_data;
thrust::device_ptr<int> r1ptr = thrust::device_pointer_cast<int>(d_result1.data());
GetSystemTime(&timer1);
LONG time_ms1 = (timer1.wSecond * 1000) + timer1.wMilliseconds;
for (int i = 0; i < NSORTS; i++)
thrust::sort(r1ptr + (i*DSIZE), r1ptr + ((i + 1)*DSIZE));
cudaDeviceSynchronize();
time_ms1 = dtime_usec(time_ms1);
std::cout << "loop time: " << time_ms1 << "ms" << std::endl;
//vectorized sort
thrust::device_vector<int> d_result2 = d_data;
thrust::host_vector<int> h_segments(DSIZE*NSORTS);
thrust::generate(h_segments.begin(), h_segments.end(), my_mod);
thrust::device_vector<int> d_segments = h_segments;
GetSystemTime(&timer1);
time_ms1 = (timer1.wSecond * 1000) + timer1.wMilliseconds;
thrust::stable_sort_by_key(d_result2.begin(), d_result2.end(), d_segments.begin());
thrust::stable_sort_by_key(d_segments.begin(), d_segments.end(), d_result2.begin());
cudaDeviceSynchronize();
time_ms1 = dtime_usec(time_ms1);
std::cout << "loop time: " << time_ms1 << "ms" << std::endl;
if (!validate(d_result1, d_result2)) std::cout << "mismatch 1!" << std::endl;
//nested sort
thrust::device_vector<int> d_result3 = d_data;
sort_functor f = { d_result3.data(), DSIZE };
thrust::device_vector<int> idxs(NSORTS);
thrust::sequence(idxs.begin(), idxs.end());
GetSystemTime(&timer1);
time_ms1 = (timer1.wSecond * 1000) + timer1.wMilliseconds;
thrust::for_each(idxs.begin(), idxs.end(), f);
cudaDeviceSynchronize();
time_ms1 = dtime_usec(time_ms1);
std::cout << "loop time: " << time_ms1 << "ms" << std::endl;
if (!validate(d_result1, d_result3)) std::cout << "mismatch 2!" << std::endl;
}
return 0;
}
回答1:
The main takeaway from your thrust experience is that you should never compile a debug project or with device debug switch (-G) when you are interested in performance. Compiling device debug code causes the compiler to omit many performance optimizations. The difference in your case was quite dramatic, about a 30x improvement going from debug to release code.
Here is a segmented cub sort, where we are launching 500 blocks and each block is handling a separate 1024 element array. The CUB code is lifted from here.
$ cat t1761.cu
#include <cub/cub.cuh> // or equivalently <cub/block/block_radix_sort.cuh>
#include <iostream>
const int ipt=8;
const int tpb=128;
__global__ void ExampleKernel(int *data)
{
// Specialize BlockRadixSort for a 1D block of 128 threads owning 8 integer items each
typedef cub::BlockRadixSort<int, tpb, ipt> BlockRadixSort;
// Allocate shared memory for BlockRadixSort
__shared__ typename BlockRadixSort::TempStorage temp_storage;
// Obtain a segment of consecutive items that are blocked across threads
int thread_keys[ipt];
// just create some synthetic data in descending order 1023 1022 1021 1020 ...
for (int i = 0; i < ipt; i++) thread_keys[i] = (tpb-1-threadIdx.x)*ipt+i;
// Collectively sort the keys
BlockRadixSort(temp_storage).Sort(thread_keys);
__syncthreads();
// write results to output array
for (int i = 0; i < ipt; i++) data[blockIdx.x*ipt*tpb + threadIdx.x*ipt+i] = thread_keys[i];
}
int main(){
const int blks = 500;
int *data;
cudaMalloc(&data, blks*ipt*tpb*sizeof(int));
ExampleKernel<<<blks,tpb>>>(data);
int *h_data = new int[blks*ipt*tpb];
cudaMemcpy(h_data, data, blks*ipt*tpb*sizeof(int), cudaMemcpyDeviceToHost);
for (int i = 0; i < 10; i++) std::cout << h_data[i] << " ";
std::cout << std::endl;
}
$ nvcc -o t1761 t1761.cu -I/path/to/cub/cub-1.8.0
$ CUDA_VISIBLE_DEVICES="2" nvprof ./t1761
==13713== NVPROF is profiling process 13713, command: ./t1761
==13713== Warning: Profiling results might be incorrect with current version of nvcc compiler used to compile cuda app. Compile with nvcc compiler 9.0 or later version to get correct profiling results. Ignore this warning if code is already compiled with the recommended nvcc version
0 1 2 3 4 5 6 7 8 9
==13713== Profiling application: ./t1761
==13713== Profiling result:
Type Time(%) Time Calls Avg Min Max Name
GPU activities: 60.35% 308.66us 1 308.66us 308.66us 308.66us [CUDA memcpy DtoH]
39.65% 202.79us 1 202.79us 202.79us 202.79us ExampleKernel(int*)
API calls: 98.39% 210.79ms 1 210.79ms 210.79ms 210.79ms cudaMalloc
0.72% 1.5364ms 1 1.5364ms 1.5364ms 1.5364ms cudaMemcpy
0.32% 691.15us 1 691.15us 691.15us 691.15us cudaLaunchKernel
0.28% 603.26us 97 6.2190us 400ns 212.71us cuDeviceGetAttribute
0.24% 516.56us 1 516.56us 516.56us 516.56us cuDeviceTotalMem
0.04% 79.374us 1 79.374us 79.374us 79.374us cuDeviceGetName
0.01% 13.373us 1 13.373us 13.373us 13.373us cuDeviceGetPCIBusId
0.00% 5.0810us 3 1.6930us 729ns 2.9600us cuDeviceGetCount
0.00% 2.3120us 2 1.1560us 609ns 1.7030us cuDeviceGet
0.00% 748ns 1 748ns 748ns 748ns cuDeviceGetUuid
$
(CUDA 10.2.89, RHEL 7)
Above I am running on a Tesla K20x, which has performance that is "closer" to your 1080ti than a Tesla V100. We see that the kernel execution time is ~200us. If I run the exact same code on a Tesla V100, the kernel execution time drops to ~35us:
$ CUDA_VISIBLE_DEVICES="0" nvprof ./t1761
==13814== NVPROF is profiling process 13814, command: ./t1761
0 1 2 3 4 5 6 7 8 9
==13814== Profiling application: ./t1761
==13814== Profiling result:
Type Time(%) Time Calls Avg Min Max Name
GPU activities: 82.33% 163.43us 1 163.43us 163.43us 163.43us [CUDA memcpy DtoH]
17.67% 35.073us 1 35.073us 35.073us 35.073us ExampleKernel(int*)
API calls: 98.70% 316.92ms 1 316.92ms 316.92ms 316.92ms cudaMalloc
0.87% 2.7879ms 1 2.7879ms 2.7879ms 2.7879ms cuDeviceTotalMem
0.19% 613.75us 97 6.3270us 389ns 205.37us cuDeviceGetAttribute
0.19% 601.61us 1 601.61us 601.61us 601.61us cudaMemcpy
0.02% 72.718us 1 72.718us 72.718us 72.718us cudaLaunchKernel
0.02% 59.905us 1 59.905us 59.905us 59.905us cuDeviceGetName
0.01% 37.886us 1 37.886us 37.886us 37.886us cuDeviceGetPCIBusId
0.00% 4.6830us 3 1.5610us 546ns 2.7850us cuDeviceGetCount
0.00% 1.9900us 2 995ns 587ns 1.4030us cuDeviceGet
0.00% 677ns 1 677ns 677ns 677ns cuDeviceGetUuid
$
You'll note there is no "input" array, I'm just synthesizing data in the kernel, since we are interested in performance, primarily. If you need to handle an array size like 1000, you should probably just pad each array to 1024 (e.g. pad with a very large number, then ignore the last numbers in the sorted result.)
来源：https://stackoverflow.com/questions/62935564/how-to-find-median-value-in-2d-array-for-each-column-with-cuda