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
65 lines
1.8 KiB
Plaintext
65 lines
1.8 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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// Iterate over every item in the hashtableA, and add them to B
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__global__ void gpu_merge_hashtable(hashtable A, const hashtable B)
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{
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unsigned int threadid = blockIdx.x * blockDim.x + threadIdx.x;
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while (threadid < reserved::kHashTableCapacity)
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{
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if (B.addr[threadid].key != reserved::kEmpty)
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{
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uint32_t value = B.addr[threadid].value;
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if (value != reserved::kEmpty)
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{
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// printf("INSERTING key %d value %d\n", pHashTableB[threadid].key, value);
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A.insert(B.addr[threadid]);
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}
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}
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threadid += blockDim.x * gridDim.x;
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}
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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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hashtable A;
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A.insert(reserved::KeyValue(107, 4));
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A.insert(reserved::KeyValue(108, 6));
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hashtable B;
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B.insert(reserved::KeyValue(7, 14));
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B.insert(reserved::KeyValue(8, 16));
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auto lA = ctx.logical_data(A);
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auto lB = ctx.logical_data(B);
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ctx.task(lA.rw(), lB.read())->*[](auto stream, auto hA, auto hB) {
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gpu_merge_hashtable<<<32, 128, 0, stream>>>(hA, hB);
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cuda_safe_call(cudaGetLastError());
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};
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ctx.host_launch(lA.read())->*[](auto hA) {
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EXPECT(hA.get(107) == 4);
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EXPECT(hA.get(108) == 6);
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EXPECT(hA.get(7) == 14);
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EXPECT(hA.get(8) == 16);
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};
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ctx.finalize();
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}
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