mirror of
https://github.com/stevenrobertson/cuburn.git
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Promising performance here.
This commit is contained in:
Steven Robertson
parent
82344d7760
commit
638d068a00
160
sortbench.cu
160
sortbench.cu
@ -27,30 +27,51 @@ void prefix_scan_8_0_shmem(unsigned char *keys, int nitems, int *pfxs) {
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}
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}
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#define GRP_RDX_FACTOR (GRPSZ / RDXSZ)
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#define GRP_BLK_FACTOR (GRPSZ / BLKSZ)
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#define GRPSZ 8192
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#define RDXSZ 256
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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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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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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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}
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__global__
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void prefix_scan_8_0_shmem_shortseg(unsigned char *keys, int *pfxs) {
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const int blksz = 256;
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const int grpsz = 8192;
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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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__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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// TODO: this introduces a hard upper limit of 512M keys (3GB) sorted in a
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// pass. It'll be a while before we get the 8GB cards needed to do this.
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int i = tid + grpsz * blockIdx.x;
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int i = tid + GRPSZ * blockIdx.x;
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for (int j = 0; j < 32; j++) {
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for (int j = 0; j < GRP_BLK_FACTOR; j++) {
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int value = keys[i];
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atomicAdd(shr_pfxs + value, 1);
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i += blksz;
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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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@ -66,10 +87,64 @@ void crappy_split(int *pfxs, int *pfxs_out) {
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}
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}
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__global__
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void better_split(int *pfxs_out, const int *pfxs) {
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// This one must be launched as 32x1, regardless of BLKSZ.
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const int tid = threadIdx.x;
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const int tid5 = tid << 5;
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__shared__ int swap[1024];
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int base = RDXSZ * 32 * blockIdx.x;
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int value = 0;
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// Performs a fast "split" (don't know why I called it that, will rename
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// soon). For each entry in pfxs (corresponding to the number of elements
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// per radix in a group), this writes the exclusive prefix sum for that
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// group. This is in fact a bunch of serial prefix sums in parallel, and
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// not a parallel prefix sum.
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//
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// The contents of 32 group radix counts are loaded in 32-element chunks
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// into shared memory, rotated by 1 unit each group to avoid bank
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// conflicts. Each thread in the warp sums across each group serially,
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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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for (int j = 0; j < 8; j++) {
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int jj = j << 5;
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for (int i = 0; i < 32; i++) {
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int base_offset = (i << 8) + jj + base + tid;
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int swap_offset = (i << 5) + ((i + tid) & 0x1f);
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swap[swap_offset] = pfxs[base_offset];
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}
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#pragma unroll
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for (int i = 0; i < 32; i++) {
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int swap_offset = tid5 + ((i + tid) & 0x1f);
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int tmp = swap[swap_offset];
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swap[swap_offset] = value;
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value += tmp;
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}
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for (int i = 0; i < 32; i++) {
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int base_offset = (i << 8) + jj + base + tid;
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int swap_offset = (i << 5) + ((i + tid) & 0x1f);
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pfxs_out[base_offset] = swap[swap_offset];
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}
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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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const int blksz = 256;
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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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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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@ -99,54 +174,73 @@ void prefix_sum(int *pfxs, int nitems, int *out_pfxs, int *out_sums) {
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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 grpsz = 8192;
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const int blksz = 256;
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const int tid = threadIdx.y * 32 + threadIdx.x;
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const int blk_offset = grpsz * blockIdx.x;
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__shared__ int shr_pfxs[blksz];
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const int blk_offset = GRPSZ * blockIdx.x;
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__shared__ int shr_pfxs[RDXSZ];
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if (threadIdx.y < 8) {
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int pfx_i = blksz * blockIdx.x + tid;
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shr_pfxs[tid] = pfxs[pfx_i];
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}
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if (tid < RDXSZ) shr_pfxs[tid] = pfxs[RDXSZ * blockIdx.x + tid];
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__syncthreads();
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int i = tid;
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for (int j = 0; j < 32; j++) {
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for (int j = 0; j < GRP_BLK_FACTOR; j++) {
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int value = keys[i+blk_offset];
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int offset = atomicAdd(shr_pfxs + value, 1);
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sorted_keys[offset] = value;
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i += blksz;
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i += BLKSZ;
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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, int *pfxs, int *split) {
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const int grpsz = 8192;
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const int blksz = 256;
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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 blk_offset = grpsz * blockIdx.x;
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__shared__ int shr_pfxs[blksz];
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__shared__ int shr_offs[blksz];
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__shared__ int defer[grpsz];
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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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const int pfx_i = blksz * blockIdx.x + tid;
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shr_pfxs[tid] = pfxs[pfx_i];
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shr_offs[tid] = split[pfx_i];
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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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for (int i = tid; i < GRPSZ; i += BLKSZ) {
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int value = keys[i+blk_offset];
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int offset = atomicAdd(shr_offs + value, 1);
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defer[offset] = value;
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}
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//shr_pfxs[tid] = pfxs[pfx_i];
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__syncthreads();
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for (int i = tid; i < grpsz; i += blksz) {
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// This calculation is a bit odd.
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//
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// For a given radix value 'r', shr_offs[r] currently holds the first index
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// of the *next* radix in defer[] (i.e. if there are 28 '0'-radix values
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// in defer[], shr_offs[0]==28). We want to get back to a normal exclusive
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// prefix, so we subtract shr_offs[0] from everything.
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//
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// In the next block, we want to be able to find the correct position for a
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// value in defer[], given that value's index 'i' and its radix 'r'. This
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// requires two values: the destination index in sorted_keys[] of the first
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// value in the group with radix 'r' (given by pfxs[BASE + r]), and the
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// number of radix-'r' values before this one in defer[]. So, ultimately,
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// we want an equation in the inner loop below that looks like this:
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//
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// int dst_offset = pfxs[r] + i - (shr_offs[r] - shr_offs[0]);
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// sorted_keys[dst_offset] = defer[i];
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//
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// Of course, this generates tons of memory lookups and bank conflicts so
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// we precombine some of this here.
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int off0 = shr_offs[0];
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if (tid < RDXSZ) shr_offs[tid] = pfxs[0] - (shr_offs[tid] - off0);
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__syncthreads();
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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_pfxs[value] + i - (shr_offs[value] - shr_offs[0]);
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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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152
sortbench.py
152
sortbench.py
@ -6,10 +6,28 @@ import pycuda.driver as cuda
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import numpy as np
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import os
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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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with open('sortbench.cu') as f: src = f.read()
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mod = pycuda.compiler.SourceModule(src, keep=True)
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def launch(name, *args, **kwargs):
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fun = mod.get_function(name)
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if kwargs.pop('l1', False):
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fun.set_cache_config(cuda.func_cache.PREFER_L1)
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if not kwargs.get('stream'):
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kwargs['time_kernel'] = True
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print 'launching %s with %sx%s... ' % (name, kwargs['block'],
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kwargs['grid']),
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t = fun(*args, **kwargs)
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if t:
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print 'done (%g secs).' % t
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else:
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print 'done.'
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def go(scale, block, test_cpu):
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data = np.fromstring(np.random.bytes(scale*block), dtype=np.uint8)
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print 'Done seeding'
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@ -21,98 +39,94 @@ def go(scale, block, test_cpu):
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print cpu_pfxs
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print 'took %g secs on CPU' % (b - a)
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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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fun = mod.get_function('prefix_scan_8_0_shmem')
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shmem_pfxs = np.zeros(256, dtype=np.int32)
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t = fun(cuda.In(data), np.int32(block), cuda.InOut(shmem_pfxs),
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block=(32, 16, 1), grid=(scale, 1), time_kernel=True)
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print 'shmem took %g secs.' % t
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launch('prefix_scan_8_0_shmem',
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cuda.In(data), np.int32(block), cuda.InOut(shmem_pfxs),
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block=(32, 16, 1), grid=(scale, 1), l1=1)
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if test_cpu:
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print 'it worked? %s' % (np.all(shmem_pfxs == cpu_pfxs))
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fun = mod.get_function('prefix_scan_8_0_shmem_lessconf')
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shmeml_pfxs = np.zeros(256, dtype=np.int32)
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t = fun(cuda.In(data), np.int32(block), cuda.InOut(shmeml_pfxs),
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block=(32, 32, 1), grid=(scale, 1), time_kernel=True)
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print 'shmeml took %g secs.' % t
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launch('prefix_scan_8_0_shmem_lessconf',
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cuda.In(data), np.int32(block), cuda.InOut(shmeml_pfxs),
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block=(32, 32, 1), grid=(scale, 1), l1=1)
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print 'it worked? %s' % (np.all(shmeml_pfxs == shmem_pfxs))
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fun = mod.get_function('prefix_scan_8_0_popc')
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popc_pfxs = np.zeros(256, dtype=np.int32)
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t = fun(cuda.In(data), np.int32(block), cuda.InOut(popc_pfxs),
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block=(32, 16, 1), grid=(scale, 1), time_kernel=True)
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print 'popc took %g secs.' % t
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print 'it worked? %s' % (np.all(shmem_pfxs == popc_pfxs))
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launch('prefix_scan_8_0_popc',
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cuda.In(data), np.int32(block), cuda.InOut(popc_pfxs),
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block=(32, 16, 1), grid=(scale, 1), l1=1)
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fun = mod.get_function('prefix_scan_5_0_popc')
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popc5_pfxs = np.zeros(32, dtype=np.int32)
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t = fun(cuda.In(data), np.int32(block), cuda.InOut(popc5_pfxs),
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block=(32, 16, 1), grid=(scale, 1), time_kernel=True)
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print 'popc5 took %g secs.' % t
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print popc5_pfxs
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launch('prefix_scan_5_0_popc',
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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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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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grids = scale * block / 8192
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print grids
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incr_pfxs = np.zeros((grids + 1, 256), dtype=np.int32)
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shortseg_pfxs = np.zeros(256, dtype=np.int32)
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shortseg_sums = np.zeros(256, dtype=np.int32)
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fun = mod.get_function('prefix_scan_8_0_shmem_shortseg')
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fun.set_cache_config(cuda.func_cache.PREFER_L1)
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t = fun(cuda.In(data), cuda.Out(incr_pfxs),
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block=(32, 8, 1), grid=(grids, 1), time_kernel=True)
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print 'shortseg took %g secs.' % t
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print incr_pfxs[0]
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print incr_pfxs[1]
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def go_sort(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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split = np.zeros((grids, 256), dtype=np.int32)
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fun = mod.get_function('crappy_split')
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fun.set_cache_config(cuda.func_cache.PREFER_L1)
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t = fun(cuda.In(incr_pfxs), cuda.Out(split),
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block=(32, 8, 1), grid=(grids / 256, 1), time_kernel=True)
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print 'crappy_split took %g secs.' % t
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print split
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grids = count / 8192
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pfxs = np.zeros((grids + 1, 256), dtype=np.int32)
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dpfxs = cuda.to_device(pfxs)
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fun = mod.get_function('prefix_sum')
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fun.set_cache_config(cuda.func_cache.PREFER_L1)
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t = fun(cuda.InOut(incr_pfxs), np.int32(grids * 256),
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cuda.Out(shortseg_pfxs), cuda.Out(shortseg_sums),
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block=(32, 8, 1), grid=(1, 1), time_kernel=True)
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print 'shortseg_sum took %g secs.' % t
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print 'it worked? %s' % (np.all(shortseg_pfxs == popc_pfxs))
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print shortseg_pfxs
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print shortseg_sums
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print incr_pfxs[1] - incr_pfxs[0]
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launch('prefix_scan_8_0_shmem_shortseg', ddata, dpfxs,
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block=(32, 16, 1), grid=(grids, 1), stream=stream, l1=1)
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sorted = np.zeros(scale * block, dtype=np.int32)
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fun = mod.get_function('sort_8')
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fun.set_cache_config(cuda.func_cache.PREFER_L1)
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t = fun(cuda.In(data), cuda.Out(sorted), cuda.In(incr_pfxs),
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block=(32, 8, 1), grid=(grids, 1), time_kernel=True)
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print 'shortseg_sort took %g secs.' % t
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print 'incr0', incr_pfxs[0]
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print sorted[:100]
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print sorted[-100:]
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#dsplit = cuda.to_device(pfxs)
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#launch('crappy_split', dpfxs, dsplit,
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#block=(32, 8, 1), grid=(grids / 256, 1), stream=stream, l1=1)
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sorted = np.zeros(scale * block, dtype=np.int32)
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fun = mod.get_function('sort_8_a')
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t = fun(cuda.In(data), cuda.Out(sorted), cuda.In(incr_pfxs), cuda.In(split),
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block=(32, 8, 1), grid=(grids, 1), time_kernel=True)
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print 'shortseg_sort took %g secs.' % t
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print 'incr0', incr_pfxs[0]
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print sorted[:100]
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print sorted[-100:]
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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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#if not stream:
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#split = cuda.from_device_like(dsplit, pfxs)
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#split_ = cuda.from_device_like(dsplit_, pfxs)
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#print np.all(split == split_)
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dshortseg_pfxs = cuda.mem_alloc(256 * 4)
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dshortseg_sums = cuda.mem_alloc(256 * 4)
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launch('prefix_sum', dpfxs, np.int32(grids * 256),
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dshortseg_pfxs, dshortseg_sums,
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block=(32, 8, 1), grid=(1, 1), stream=stream, l1=1)
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dsorted = cuda.mem_alloc(count * 4)
|
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launch('sort_8', ddata, dsorted, dpfxs,
|
||||
block=(32, 16, 1), grid=(grids, 1), stream=stream, l1=1)
|
||||
|
||||
launch('sort_8_a', ddata, dsorted, dpfxs, dsplit,
|
||||
block=(32, 32, 1), grid=(grids, 1), stream=stream)
|
||||
if not stream:
|
||||
sorted = cuda.from_device(dsorted, (count,), np.int32)
|
||||
f = lambda r: ''.join(['\n\t%3d %4d %4d' % v for v in r])
|
||||
sort_stat = f(rle(sorted))
|
||||
with open('dev.txt', 'w') as fp: fp.write(sort_stat)
|
||||
|
||||
sorted_np = np.sort(data)
|
||||
np_stat = f(rle(sorted_np))
|
||||
with open('cpu.txt', 'w') as fp: fp.write(np_stat)
|
||||
|
||||
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(1024, 512<<8, False)
|
||||
#go(32768, 8192, False)
|
||||
|
||||
stream = cuda.Stream() if '-s' in sys.argv else None
|
||||
go_sort(128<<20, stream)
|
||||
if stream:
|
||||
stream.synchronize()
|
||||
|
||||
main()
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user