木质
发表于 2025-3-25 05:25:30
Optimal GPU-CPU Offloading Strategies for Deep Neural Network Trainingnd requires to determine which activations should be offloaded and when these transfers should take place. We prove that this problem is NP-complete in the strong sense, and propose two heuristics based on relaxations of the problem. We then conduct a thorough experimental evaluation of standard deep neural networks.
下级
发表于 2025-3-25 10:20:19
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任意
发表于 2025-3-25 12:20:57
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MIR
发表于 2025-3-25 19:53:56
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maverick
发表于 2025-3-25 22:04:01
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fender
发表于 2025-3-26 01:25:14
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俗艳
发表于 2025-3-26 07:13:35
https://doi.org/10.1007/978-3-642-94213-6e the others are throttled. The overall execution performance is improved. Employing the . on diverse HPC benchmarks and real-world applications, we observed that the hardware settings adjusted by . have near-optimal results compared to the optimal setting of a static approach. The achieved speedup in our work amounts to up to 6.3%.
厨房里面
发表于 2025-3-26 09:46:29
Die Revision der Neurosenfrage,underlying parallel programming model and implemented our optimization framework in the LLVM toolchain. We evaluated it with ten benchmarks and obtained a geometric speedup of 2.3., and reduced on average 50% of the total bytes transferred between the host and GPU.
迎合
发表于 2025-3-26 13:02:57
Marc Oliver Opresnik,Oguz Yilmazayers from state-of-the-art CNNs on two different GPU platforms, NVIDIA TITAN Xp and Tesla P4. The experiments show that the average speedup is 2.02 . on representative structures of CNNs, and 1.57. on end-to-end inference of SqueezeNet.
围裙
发表于 2025-3-26 20:31:46
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