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
21 lines
611 B
Python
21 lines
611 B
Python
import logging
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class Logger:
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_instance = None
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def __new__(cls, *args, **kwargs):
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if cls._instance is None:
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cls._instance = super().__new__(cls, *args, **kwargs)
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logger = logging.getLogger()
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logger.setLevel(logging.DEBUG)
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file_handler = logging.FileHandler("cccl_meta_bench.log")
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file_handler.setFormatter(logging.Formatter("%(asctime)s: %(message)s"))
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logger.addHandler(file_handler)
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cls._instance.logger = logger
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return cls._instance
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def info(self, message):
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self.logger.info(message)
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