first commit alternative radix
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Evghenii
265
examples_ptx/radixSort/radix.ispc
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265
examples_ptx/radixSort/radix.ispc
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//----------------------------------------------------------------------------
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// scan4 scans 4*RadixSort::CTA_SIZE numElements in a block (4 per thread), using
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// a warp-scan algorithm
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//----------------------------------------------------------------------------
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struct int4 { int x,y,z,w; };
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struct int2 { int x,y; };
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static int4 scan4(int4 idata)
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{
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int idx = programIndex;
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int4 val4 = idata;
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int sum[3];
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sum[0] = val4.x;
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sum[1] = val4.y + sum[0];
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sum[2] = val4.z + sum[1];
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int val = val4.w + sum[2];
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val = exclusive_scan_add(val);
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val4.x = val;
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val4.y = val + sum[0];
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val4.z = val + sum[1];
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val4.w = val + sum[2];
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return val4;
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}
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static int4 rank4(int4 preds)
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{
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int localId = programIndex;
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uniform int localSize = programCount;
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int4 address = scan4(preds);
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const int numtrue = broadcast(address.w + preds.w, localSize - 1);
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int4 rank;
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int idx = localId*4;
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rank.x = (preds.x) ? address.x : numtrue + idx - address.x;
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rank.y = (preds.y) ? address.y : numtrue + idx + 1 - address.y;
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rank.z = (preds.z) ? address.z : numtrue + idx + 2 - address.z;
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rank.w = (preds.w) ? address.w : numtrue + idx + 3 - address.w;
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return rank;
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}
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static int4 radixSortBlockKeysOnly(
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int4 key,
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uniform int nbits,
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uniform int startbit,
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uniform int sMem[],
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uniform int numtrue[])
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{
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int localId = programIndex;
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uniform int localSize = programCount;
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for (uniform int shift = startbit; shift < (startbit + nbits); ++shift)
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{
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int4 lsb;
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lsb.x = !(((key).x >> shift) & 0x1);
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lsb.y = !(((key).y >> shift) & 0x1);
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lsb.z = !(((key).z >> shift) & 0x1);
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lsb.w = !(((key).w >> shift) & 0x1);
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int4 r;
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r = rank4(lsb);
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// This arithmetic strides the ranks across 4 CTA_SIZE regions
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sMem[(r.x & 3) * localSize + (r.x >> 2)] = (key).x;
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sMem[(r.y & 3) * localSize + (r.y >> 2)] = (key).y;
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sMem[(r.z & 3) * localSize + (r.z >> 2)] = (key).z;
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sMem[(r.w & 3) * localSize + (r.w >> 2)] = (key).w;
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// The above allows us to read without 4-way bank conflicts:
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(key).x = sMem[localId];
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(key).y = sMem[localId + localSize];
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(key).z = sMem[localId + 2 * localSize];
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(key).w = sMem[localId + 3 * localSize];
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}
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return key;
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}
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task
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void radixSortBlocksKeysOnly(
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uniform int4 keysIn[],
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uniform int4 keysOut[],
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uniform int nbits,
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uniform int startbit,
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uniform int numElements,
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uniform int totalBlocks,
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uniform int sMem[])
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{
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int globalId = taskIndex*programCount + programIndex;
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uniform int numtrue[1];
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int4 key;
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key = keysIn[globalId];
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key = radixSortBlockKeysOnly(key, nbits, startbit, sMem, numtrue);
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keysOut[globalId] = key;
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}
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//----------------------------------------------------------------------------
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// Given an array with blocks sorted according to a 4-bit radix group, each
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// block counts the number of keys that fall into each radix in the group, and
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// finds the starting offset of each radix in the block. It then writes the radix
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// counts to the counters array, and the starting offsets to the blockOffsets array.
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//
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// Template parameters are used to generate efficient code for various special cases
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// For example, we have to handle arrays that are a multiple of the block size
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// (fullBlocks) differently than arrays that are not. "loop" is used when persistent
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// CTAs are used.
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//
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// By persistent CTAs we mean that we launch only as many thread blocks as can
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// be resident in the GPU and no more, rather than launching as many threads as
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// we have elements. Persistent CTAs loop over blocks of elements until all work
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// is complete. This can be faster in some cases. In our tests it is faster
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// for large sorts (and the threshold is higher on compute version 1.1 and earlier
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// GPUs than it is on compute version 1.2 GPUs.
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//
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//----------------------------------------------------------------------------
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task
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void findRadixOffsets(
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uniform int2 keys[],
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uniform int counters[],
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uniform int blockOffsets[],
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uniform int startbit,
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uniform int numElements,
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uniform int totalBlocks,
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uniform int sRadix1[])
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{
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uniform int sStartPointers[16];
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uniform int groupId = taskIndex;
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uniform int groupSize = programCount;
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int localId = programIndex;
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int2 radix2;
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int globalId = taskIndex*programCount + programIndex;
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radix2 = keys[globalId];
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sRadix1[2 * localId] = (radix2.x >> startbit) & 0xF;
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sRadix1[2 * localId + 1] = (radix2.y >> startbit) & 0xF;
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// Finds the position where the sRadix1 entries differ and stores start
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// index for each radix.
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if(localId < 16)
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sStartPointers[localId] = 0;
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if((localId > 0) && (sRadix1[localId] != sRadix1[localId - 1]) )
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sStartPointers[sRadix1[localId]] = localId;
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if(sRadix1[localId + groupSize] != sRadix1[localId + groupSize - 1])
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sStartPointers[sRadix1[localId + groupSize]] = localId + groupSize;
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if(localId < 16)
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blockOffsets[groupId*16 + localId] = sStartPointers[localId];
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// Compute the sizes of each block.
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if((localId > 0) && (sRadix1[localId] != sRadix1[localId - 1]) )
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sStartPointers[sRadix1[localId - 1]] =
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localId - sStartPointers[sRadix1[localId - 1]];
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if(sRadix1[localId + groupSize] != sRadix1[localId + groupSize - 1] )
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sStartPointers[sRadix1[localId + groupSize - 1]] =
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localId + groupSize - sStartPointers[sRadix1[localId + groupSize - 1]];
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if(localId == groupSize - 1)
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sStartPointers[sRadix1[2 * groupSize - 1]] =
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2 * groupSize - sStartPointers[sRadix1[2 * groupSize - 1]];
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if(localId < 16)
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counters[localId * totalBlocks + groupId] = sStartPointers[localId];
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}
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// a naive scan routine that works only for array that
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// can fit into a single block, just for debugging purpose,
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// not used in the sort now
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task
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void scanNaive(
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uniform int g_odata[],
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uniform int g_idata[],
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uniform int n,
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uniform int temp[])
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{
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if (programIndex < n)
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g_odata[programIndex] = exclusive_scan_add(g_idata[programIndex]);
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}
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//----------------------------------------------------------------------------
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// reorderData shuffles data in the array globally after the radix offsets
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// have been found. On compute version 1.1 and earlier GPUs, this code depends
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// on RadixSort::CTA_SIZE being 16 * number of radices (i.e. 16 * 2^nbits).
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//
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// On compute version 1.1 GPUs ("manualCoalesce=true") this function ensures
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// that all writes are coalesced using extra work in the kernel. On later
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// GPUs coalescing rules have been relaxed, so this extra overhead hurts
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// performance. On these GPUs we set manualCoalesce=false and directly store
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// the results.
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//
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// Template parameters are used to generate efficient code for various special cases
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// For example, we have to handle arrays that are a multiple of the block size
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// (fullBlocks) differently than arrays that are not. "loop" is used when persistent
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// CTAs are used.
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//
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// By persistent CTAs we mean that we launch only as many thread blocks as can
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// be resident in the GPU and no more, rather than launching as many threads as
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// we have elements. Persistent CTAs loop over blocks of elements until all work
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// is complete. This can be faster in some cases. In our tests it is faster
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// for large sorts (and the threshold is higher on compute version 1.1 and earlier
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// GPUs than it is on compute version 1.2 GPUs.
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//----------------------------------------------------------------------------
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task
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void reorderDataKeysOnly(
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uniform int outKeys[],
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uniform int2 keys[],
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uniform int blockOffsets[],
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uniform int offsets[],
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uniform int sizes[],
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uniform int startbit,
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uniform int numElements,
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uniform int totalBlocks,
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uniform int2 sKeys2[])
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{
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uniform int sOffsets[16];
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uniform int sBlockOffsets[16];
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uniform int * uniform sKeys1 = (uniform int* uniform)sKeys2;
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uniform int groupId = taskIndex;
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uniform int groupSize = programCount;
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int localId = programIndex;
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int globalId = taskIndex*programCount + programIndex;
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sKeys2[localId] = keys[globalId];
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if(localId < 16)
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{
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sOffsets[localId] = offsets[localId * totalBlocks + groupId];
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sBlockOffsets[localId] = blockOffsets[groupId * 16 + localId];
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}
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int radix = (sKeys1[localId] >> startbit) & 0xF;
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int globalOffset = sOffsets[radix] + localId - sBlockOffsets[radix];
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if (globalOffset < numElements)
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outKeys[globalOffset] = sKeys1[localId];
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radix = (sKeys1[localId + groupSize] >> startbit) & 0xF;
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globalOffset = sOffsets[radix] + localId + groupSize - sBlockOffsets[radix];
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if (globalOffset < numElements)
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outKeys[globalOffset] = sKeys1[localId + groupSize];
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}
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