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
27 lines
530 B
C++
27 lines
530 B
C++
#pragma once
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// for demangling the result of type_info.name()
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// with msvc, type_info.name() is already demangled
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#ifdef __GNUC__
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# include <cxxabi.h>
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#endif // __GNUC__
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#include <cstdlib>
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#include <string>
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namespace unittest
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{
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inline std::string demangle(const char* name)
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{
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#if __GNUC__ && !_NVHPC_CUDA
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int status = 0;
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char* realname = abi::__cxa_demangle(name, nullptr, nullptr, &status);
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std::string result(realname);
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std::free(realname);
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return result;
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#else
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return name;
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#endif
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}
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} // namespace unittest
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