mirror of
https://github.com/stevenrobertson/cuburn.git
synced 2025-11-03 18:00:55 -05:00
Fine performance, but the scan's mis-ordering is worse than I thought.
This commit is contained in:
Steven Robertson
149
sortbench.cu
149
sortbench.cu
@ -1,6 +1,8 @@
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#include <cuda.h>
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#include <stdio.h>
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#define s(x) #x
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__global__
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void prefix_scan_8_0_shmem(unsigned char *keys, int nitems, int *pfxs) {
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__shared__ int sh_pfxs[256];
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@ -34,22 +36,26 @@ void prefix_scan_8_0_shmem(unsigned char *keys, int nitems, int *pfxs) {
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#define BLKSZ 512
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__global__
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void prefix_scan(unsigned short *keys, int *pfxs, const int shift) {
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const int tid = threadIdx.y * 32 + threadIdx.x;
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__shared__ int shr_pfxs[BLKSZ];
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void prefix_scan(unsigned short *offsets, int *pfxs,
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const unsigned short *keys, const int shift) {
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const int tid = threadIdx.x;
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__shared__ int shr_pfxs[RDXSZ];
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shr_pfxs[tid] = 0;
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if (tid < RDXSZ) shr_pfxs[tid] = 0;
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__syncthreads();
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int i = tid + GRPSZ * blockIdx.x;
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for (int j = 0; j < GRP_BLK_FACTOR; j++) {
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int value = (keys[i] >> shift) && 0xff;
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atomicAdd(shr_pfxs + value, 1);
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// TODO: compiler smart enough to turn this into a BFE?
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// TODO: should this just be two functions with fixed shifts?
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// TODO: separate or integrated loop vars? unrolling?
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int value = (keys[i] >> shift) & 0xff;
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offsets[i] = atomicAdd(shr_pfxs + value, 1);
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i += BLKSZ;
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}
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__syncthreads();
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pfxs[tid + BLKSZ * blockIdx.x] = shr_pfxs[tid];
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if (tid < RDXSZ) pfxs[tid + RDXSZ * blockIdx.x] = shr_pfxs[tid];
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}
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__global__
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@ -110,10 +116,9 @@ void better_split(int *pfxs_out, const int *pfxs) {
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// updating the values as it goes, then the results are written coherently
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// to global memory.
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//
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// This leaves the processor extremely compute-starved, as this only allows
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// 12 warps per SM. It might be better to halve the chunk size and lose
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// some coalescing efficiency; need to benchmark. It's a relatively cheap
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// step overall though.
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// This leaves the SM underloaded, as this only allows 12 warps per SM. It
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// might be better to halve the chunk size and lose some coalescing
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// efficiency; need to benchmark. It's a relatively cheap step, though.
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for (int j = 0; j < 8; j++) {
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int jj = j << 5;
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@ -139,17 +144,16 @@ void better_split(int *pfxs_out, const int *pfxs) {
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}
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}
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__global__
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void prefix_sum(int *pfxs, int nitems, int *out_pfxs, int *out_sums) {
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void prefix_sum(int *pfxs, const int nitems) {
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// Needs optimizing (later). Should be rolled into split.
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// Must launch 32x8.
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const int tid = threadIdx.y * 32 + threadIdx.x;
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// Must launch 256 threads.
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const int tid = threadIdx.x;
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const int blksz = 256;
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int val = 0;
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for (int i = tid; i < nitems; i += blksz) val += pfxs[i];
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out_pfxs[tid] = val;
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// I know there's a better way to implement this summing network,
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// but it's not a time-critical piece of code.
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__shared__ int sh_pfxs[blksz];
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@ -158,23 +162,18 @@ void prefix_sum(int *pfxs, int nitems, int *out_pfxs, int *out_sums) {
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__syncthreads();
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// Intentionally exclusive indexing here, val{0} should be 0
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for (int i = 0; i < tid; i++) val += sh_pfxs[i];
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out_sums[tid] = val;
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// Here we shift things over by 1, to make retrieving the
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// indices and differences easier in the sorting step.
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int i;
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for (i = tid; i < nitems; i += blksz) {
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int t = pfxs[i];
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pfxs[i] = val;
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val += t;
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}
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// Now write the last column and we're done.
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pfxs[i] = val;
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}
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__global__
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void sort_8(unsigned char *keys, int *sorted_keys, int *pfxs) {
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const int tid = threadIdx.y * 32 + threadIdx.x;
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const int tid = threadIdx.x;
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const int blk_offset = GRPSZ * blockIdx.x;
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__shared__ int shr_pfxs[RDXSZ];
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@ -190,12 +189,13 @@ void sort_8(unsigned char *keys, int *sorted_keys, int *pfxs) {
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}
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}
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#undef BLKSZ
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#define BLKSZ 1024
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__global__
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void sort_8_a(unsigned char *keys, int *sorted_keys,
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const int *pfxs, const int *split) {
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const int tid = threadIdx.y * 32 + threadIdx.x;
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const int tid = threadIdx.x;
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const int blk_offset = GRPSZ * blockIdx.x;
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__shared__ int shr_offs[RDXSZ];
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__shared__ int defer[GRPSZ];
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@ -244,6 +244,109 @@ void sort_8_a(unsigned char *keys, int *sorted_keys,
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}
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}
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__global__
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void convert_offsets(
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unsigned short *offsets, // input and output
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const int *split,
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const unsigned short *keys,
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const int shift
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) {
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const int tid = threadIdx.x;
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const int blk_offset = GRPSZ * blockIdx.x;
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const int rdx_offset = RDXSZ * blockIdx.x;
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__shared__ int shr_offsets[GRPSZ];
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__shared__ int shr_split[RDXSZ];
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if (tid < RDXSZ) shr_split[tid] = split[rdx_offset + tid];
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__syncthreads();
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for (int i = tid; i < GRPSZ; i += BLKSZ) {
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int r = (keys[blk_offset + i] >> shift) & 0xff;
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int o = shr_split[r] + offsets[blk_offset + i];
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shr_offsets[o] = i;
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}
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__syncthreads();
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for (int i = tid; i < GRPSZ; i += BLKSZ)
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offsets[blk_offset + i] = shr_offsets[i];
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}
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__global__
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void radix_sort_maybe(
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unsigned short *sorted_keys,
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int *sorted_values,
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const unsigned short *keys,
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const unsigned int *values,
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const unsigned short *offsets,
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const int *pfxs,
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const int *split,
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const int shift
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) {
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const int tid = threadIdx.x;
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const int blk_offset = GRPSZ * blockIdx.x;
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const int rdx_offset = RDXSZ * blockIdx.x;
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__shared__ int shr_offs[RDXSZ];
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if (tid < RDXSZ)
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shr_offs[tid] = pfxs[rdx_offset + tid] - split[rdx_offset + tid];
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__syncthreads();
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int i = tid;
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for (int j = 0; j < GRP_BLK_FACTOR; j++) {
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int offset = offsets[blk_offset + i];
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int key = keys[blk_offset + offset];
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int radix = (key >> shift) & 0xff;
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int glob_offset = shr_offs[radix] + i;
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/*if (sorted_values[glob_offset] != 0xffffffff)
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printf("\nbad offset pos:%6x off:%4x gloff:%6x key:%4x "
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"okey:%4x val:%8x oval:%8x",
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i+blk_offset, offset, glob_offset, key,
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sorted_keys[glob_offset], sorted_values[glob_offset]);*/
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sorted_keys[glob_offset] = key;
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sorted_values[glob_offset] = values[blk_offset + offset];
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i += BLKSZ;
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}
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}
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__global__
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void radix_sort(unsigned short *sorted_keys, int *sorted_values,
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const unsigned short *keys, const unsigned int *values,
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const int *pfxs, const int *offsets, const int *split,
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const int shift) {
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const int tid = threadIdx.x;
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const int blk_offset = GRPSZ * blockIdx.x;
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__shared__ int shr_offs[RDXSZ];
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__shared__ int defer[GRPSZ];
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__shared__ unsigned char radishes[GRPSZ];
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const int pfx_i = RDXSZ * blockIdx.x + tid;
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if (tid < RDXSZ) shr_offs[tid] = split[pfx_i];
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__syncthreads();
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for (int i = tid; i < GRPSZ; i += BLKSZ) {
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int idx = i + blk_offset;
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int value = keys[idx];
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int radix = radishes[i] = (value >> shift) & 0xff;
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int offset = offsets[idx] + split[radix];
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defer[offset] = value;
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}
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__syncthreads();
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if (tid < RDXSZ) shr_offs[tid] = pfxs[tid] - shr_offs[tid];
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__syncthreads();
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// Faster to reload these or to recompute them in shmem? Need to see if we
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// can safely stash both
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int i = tid;
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#pragma unroll
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for (int j = 0; j < GRP_BLK_FACTOR; j++) {
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int value = defer[i];
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int offset = shr_offs[value] + i;
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sorted_keys[offset] = value;
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i += BLKSZ;
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}
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}
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__global__
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163
sortbench.py
163
sortbench.py
@ -5,11 +5,14 @@ import pycuda.compiler
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import pycuda.driver as cuda
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import numpy as np
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np.set_printoptions(precision=5, edgeitems=20, linewidth=100, threshold=9000)
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import sys, os
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os.environ['PATH'] = ('/usr/x86_64-pc-linux-gnu/gcc-bin/4.4.6:'
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+ os.environ['PATH'])
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i32 = np.int32
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with open('sortbench.cu') as f: src = f.read()
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mod = pycuda.compiler.SourceModule(src, keep=True)
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@ -62,12 +65,161 @@ def go(scale, block, test_cpu):
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cuda.In(data), np.int32(block), cuda.InOut(popc5_pfxs),
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block=(32, 16, 1), grid=(scale, 1), l1=1)
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def rle(a):
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def rle(a, n=512):
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pos, = np.where(np.diff(a))
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lens = np.diff(np.concatenate((pos, [len(a)])))
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return [(a[p], p, l) for p, l in zip(pos, lens)[:5000]]
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pos = np.concatenate(([0], pos+1, [len(a)]))
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lens = np.diff(pos)
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return [(a[p], p, l) for p, l in zip(pos, lens)[:n]]
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def frle(a, n=512):
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return ''.join(['\n\t%4x %6x %6x' % v for v in rle(a, n)])
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# Some reference implementations follow for debugging.
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def py_convert_offsets(offsets, split, keys, shift):
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grids = len(offsets)
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new_offs = np.empty((grids, 8192), dtype=np.int32)
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for i in range(grids):
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rdxs = (keys[i] >> shift) & 0xff
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o = split[i][rdxs] + offsets[i]
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new_offs[i][o] = np.arange(8192, dtype=np.int32)
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return new_offs
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def py_radix_sort_maybe(keys, offsets, pfxs, split, shift):
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grids = len(offsets)
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idxs = np.arange(8192)
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okeys = np.empty(grids*8192, dtype=np.int32)
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okeys.fill(-1)
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for i in range(grids):
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offs = pfxs[i] - split[i]
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lkeys = keys[i][offsets[i]]
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rdxs = (lkeys >> shift) & 0xff
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glob_offsets = offs[rdxs] + idxs
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okeys[glob_offsets] = lkeys
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return okeys
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def go_sort(count, stream=None):
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grids = count / 8192
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#keys = np.fromstring(np.random.bytes(count*2), dtype=np.uint16)
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keys = np.arange(count, dtype=np.uint16)
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np.random.shuffle(keys)
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mkeys = np.reshape(keys, (grids, 8192))
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vals = np.arange(count, dtype=np.uint32)
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dkeys = cuda.to_device(keys)
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dvals = cuda.to_device(vals)
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print 'Done seeding'
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dpfxs = cuda.mem_alloc(grids * 256 * 4)
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doffsets = cuda.mem_alloc(count * 2)
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launch('prefix_scan', doffsets, dpfxs, dkeys, i32(0),
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block=(512, 1, 1), grid=(grids, 1), stream=stream, l1=1)
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print cuda.from_device(dpfxs, (2, 256), np.uint32)
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dsplit = cuda.mem_alloc(grids * 256 * 4)
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launch('better_split', dsplit, dpfxs,
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block=(32, 1, 1), grid=(grids / 32, 1), stream=stream)
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# This stage will be rejiggered along with the split
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launch('prefix_sum', dpfxs, np.int32(grids * 256),
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block=(256, 1, 1), grid=(1, 1), stream=stream, l1=1)
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print cuda.from_device(dpfxs, (2, 256), np.uint32)
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launch('convert_offsets', doffsets, dsplit, dkeys, i32(0),
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block=(1024, 1, 1), grid=(grids, 1), stream=stream)
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if not stream:
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offsets = cuda.from_device(doffsets, (grids, 8192), np.uint16)
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split = cuda.from_device(dsplit, (grids, 256), np.uint32)
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pfxs = cuda.from_device(dpfxs, (grids, 256), np.uint32)
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tkeys = py_radix_sort_maybe(mkeys, offsets, pfxs, split, 0)
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print frle(tkeys & 0xff)
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d_skeys = cuda.mem_alloc(count * 2)
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d_svals = cuda.mem_alloc(count * 4)
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if not stream:
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cuda.memset_d32(d_skeys, 0, count/2)
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cuda.memset_d32(d_svals, 0xffffffff, count)
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launch('radix_sort_maybe', d_skeys, d_svals,
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dkeys, dvals, doffsets, dpfxs, dsplit, i32(0),
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block=(1024, 1, 1), grid=(grids, 1), stream=stream, l1=1)
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if not stream:
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skeys = cuda.from_device_like(d_skeys, keys)
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svals = cuda.from_device_like(d_svals, vals)
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# Test integrity of sort (keys and values kept together):
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# skeys[i] = keys[svals[i]] for all i
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print 'Integrity: ',
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if np.all(svals < len(keys)) and np.all(skeys == keys[svals]):
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print 'pass'
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else:
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print 'FAIL'
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print frle(skeys & 0xff)
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dkeys, d_skeys = d_skeys, dkeys
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dvals, d_svals = d_svals, dvals
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if not stream:
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cuda.memset_d32(d_skeys, 0, count/2)
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cuda.memset_d32(d_svals, 0xffffffff, count)
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launch('prefix_scan', doffsets, dpfxs, dkeys, i32(8),
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block=(512, 1, 1), grid=(grids, 1), stream=stream, l1=1)
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launch('better_split', dsplit, dpfxs,
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block=(32, 1, 1), grid=(grids / 32, 1), stream=stream)
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launch('prefix_sum', dpfxs, np.int32(grids * 256),
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block=(256, 1, 1), grid=(1, 1), stream=stream, l1=1)
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pre_offsets = cuda.from_device(doffsets, (grids, 8192), np.uint16)
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launch('convert_offsets', doffsets, dsplit, dkeys, i32(8),
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block=(1024, 1, 1), grid=(grids, 1), stream=stream)
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if not stream:
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offsets = cuda.from_device(doffsets, (grids, 8192), np.uint16)
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split = cuda.from_device(dsplit, (grids, 256), np.uint32)
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pfxs = cuda.from_device(dpfxs, (grids, 256), np.uint32)
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tkeys = np.reshape(tkeys, (grids, 8192))
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new_offs = py_convert_offsets(pre_offsets, split, tkeys, 8)
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print new_offs[:3]
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print offsets[:3]
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print np.nonzero(new_offs != offsets)
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fkeys = py_radix_sort_maybe(tkeys, new_offs, pfxs, split, 8)
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print frle(fkeys)
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launch('radix_sort_maybe', d_skeys, d_svals,
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dkeys, dvals, doffsets, dpfxs, dsplit, i32(8),
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block=(1024, 1, 1), grid=(grids, 1), stream=stream, l1=1)
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if not stream:
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#print cuda.from_device(doffsets, (4, 8192), np.uint16)
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#print cuda.from_device(dkeys, (4, 8192), np.uint16)
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#print cuda.from_device(d_skeys, (4, 8192), np.uint16)
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skeys = cuda.from_device_like(d_skeys, keys)
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svals = cuda.from_device_like(d_svals, vals)
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print 'Integrity: ',
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if np.all(svals < len(keys)) and np.all(skeys == keys[svals]):
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print 'pass'
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else:
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print 'FAIL'
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sorted_keys = np.sort(keys)
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# Test that ordering is correct. (Note that we don't need 100%
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# correctness, so this test should be made "soft".)
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print 'Order: ', 'pass' if np.all(skeys == sorted_keys) else 'FAIL'
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print frle(skeys)
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print fkeys
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print skeys
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print np.nonzero(fkeys != skeys)[0]
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def go_sort_old(count, stream=None):
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data = np.fromstring(np.random.bytes(count), dtype=np.uint8)
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ddata = cuda.to_device(data)
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print 'Done seeding'
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@ -115,16 +267,13 @@ def go_sort(count, stream=None):
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|
||||
print 'is_sorted?', np.all(sorted == sorted_np)
|
||||
|
||||
#data = np.fromstring(np.random.bytes(scale*block), dtype=np.uint16)
|
||||
|
||||
|
||||
def main():
|
||||
# shmem is known good; disable the CPU run to get better info from cuprof
|
||||
#go(8, 512<<10, True)
|
||||
#go(1024, 512<<8, False)
|
||||
#go(32768, 8192, False)
|
||||
stream = cuda.Stream() if '-s' in sys.argv else None
|
||||
go_sort(128<<20, stream)
|
||||
go_sort(1<<20, stream)
|
||||
if stream:
|
||||
stream.synchronize()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user