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TorchBench: Benchmarking PyTorch with High API Surface Coverage

Yueming Hao, Xu Zhao, Bin Bao, David Berard, Will Constable, Adnan Aziz, Xu Liu

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Tags: AMD Radeon Instinct MI210, ATI, Benchmarking, Computer science, Deep learning, nVidia, nVidia A100, Package, PyTorch

May 14, 2023 by hgpu

BUDDY MLIR

Compiler Technologies in Deep Learning Co-Design: A Survey

pocl: Portable Computing Language (IWOCL23 release)

Implementation Techniques for SPMD Kernels on CPUs

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

AutoDock for GPUs and other accelerators

Experiences Migrating CUDA to SYCL: A Molecular Docking Case Study

spotPython: Sequential Parameter Optimization in Python

PyTorch Hyperparameter Tuning – A Tutorial for spotPython

Test benchmarks for CELERITY

An Asynchronous Dataflow-Driven Execution Model For Distributed Accelerator Computing

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Descend: an attempt at safe imperative GPU programming

Descend: A Safe GPU Systems Programming Language

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