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Tools for top-down performance analysis of GPU-accelerated applications.

, , and . ICS, page 26:1-26:12. ACM, (2020)

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Tools for top-down performance analysis of GPU-accelerated applications., , and . ICS, page 26:1-26:12. ACM, (2020)A tool for top-down performance analysis of GPU-accelerated applications., , and . PPoPP, page 415-416. ACM, (2020)Centimani: Enabling Fast AI Accelerator Selection for DNN Training with a Novel Performance Predictor., , , , , , , and . USENIX ATC, page 1203-1221. USENIX Association, (2024)Low overhead and context sensitive profiling of CPU-accelerated applications., , , and . ICS, page 1:1-1:13. ACM, (2022)Understanding the GPU Microarchitecture to Achieve Bare-Metal Performance Tuning., , , , , and . PPoPP, page 31-43. ACM, (2017)Multi-Classes Feature Engineering with Sliding Window for Purchase Prediction in Mobile Commerce., , , and . ICDM Workshops, page 1048-1054. IEEE Computer Society, (2015)A Tool for Performance Analysis of GPU-Accelerated Applications., and . CGO, page 282. IEEE, (2019)GVProf: a value profiler for GPU-based clusters., , , , and . SC, page 89. IEEE/ACM, (2020)Measurement and Analysis of GPU-Accelerated OpenCL Computations on Intel GPUs., , , , and . ProTools@SC, page 26-35. IEEE, (2021)GPA: A GPU Performance Advisor Based on Instruction Sampling., , , and . CGO, page 115-125. IEEE, (2021)