56fd68e7dd
CCCL (CUDA C++ Core Libraries) provides: - CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk) - Thrust: high-level parallel algorithms (transform_reduce, sort, scan) - libcudacxx: CUDA C++ standard library (atomics, barriers, memory) - cudax: experimental features (memory resources, allocators) - Tuning policies: per-SM hardware-specific algorithm parameters Competition optimization vectors mapped to CCCL: - Output TPS (83% weight): warp_reduce, block_reduce, device_topk - Input TPS (14% weight): device_scan, block_load, prefetch - Cache TPS (3% weight): prefix caching strategy patterns - Memory (0.9 util): pooled/cached/buddy allocators Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only) License: Apache-2.0
66 lines
2.2 KiB
Plaintext
66 lines
2.2 KiB
Plaintext
//===----------------------------------------------------------------------===//
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//
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// Part of CUDASTF in CUDA C++ Core Libraries,
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// under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
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//
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//===----------------------------------------------------------------------===//
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#include <cuda/experimental/__stf/stream/interfaces/hashtable_linearprobing.cuh>
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#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
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using namespace cuda::experimental::stf;
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// Insert the key/values in kvs into the hashtable
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__global__ void gpu_hashtable_insert_kernel(hashtable h, const reserved::KeyValue* kvs, unsigned int numkvs)
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{
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unsigned int threadid = blockIdx.x * blockDim.x + threadIdx.x;
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if (threadid < numkvs)
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{
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h.insert(kvs[threadid]);
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}
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}
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void gpu_hashtable_insert(hashtable d_h, const reserved::KeyValue* device_kvs, unsigned int num_kvs, cudaStream_t stream)
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{
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// Have CUDA calculate the thread block size
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const auto occ_res = reserved::compute_occupancy(gpu_hashtable_insert_kernel);
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int threadblocksize = occ_res.block_size;
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int gridsize = ((uint32_t) num_kvs + threadblocksize - 1) / threadblocksize;
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// Insert all the keys into the hash table
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gpu_hashtable_insert_kernel<<<gridsize, threadblocksize, 0, stream>>>(d_h, device_kvs, (uint32_t) num_kvs);
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}
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int main()
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{
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stream_ctx ctx;
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// This constructor automatically initializes an empty hashtable on the host
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hashtable h;
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auto lh = ctx.logical_data(h);
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// Create an array of values on the host
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reserved::KeyValue kvs_array[16];
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for (uint32_t i = 0; i < 16; i++)
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{
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kvs_array[i].key = i * 10;
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kvs_array[i].value = 17 + i * 14;
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}
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auto h_kvs_array = ctx.logical_data(make_slice(&kvs_array[0], 16));
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ctx.task(lh.rw(), h_kvs_array.read())->*[](auto stream, auto h, auto a) {
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gpu_hashtable_insert(h, a.data_handle(), static_cast<uint32_t>(a.extent(0)), stream);
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};
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ctx.finalize();
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// Thanks to the write-back mechanism on lh, h has been updated
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for (size_t i = 0; i < 16; i++)
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{
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assert(h.get(i * 10) == 17 + i * 14);
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}
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}
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