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DISC: A Dynamic Shape Compiler for Machine Learning Workloads

Kai Zhu, Wenyi Zhao, Zhen Zheng, Tianyou Guo, Pengzhan Zhao, Junjie Bai, Jun Yang, Xiaoyong Liu, Lansong Diao, Wei Lin

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Tags: Compilers, Comuter science, CUDA, Machine learning, nVidia, Tesla T4

March 14, 2021 by hgpu

Performance Improvement of Data Mining in Weka through GPU Acceleration

Tiago Augusto Engel, Andrea Schwertner Charao, Manuele Kirsch-Pinheiro, Luiz-Angelo Steffenel

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Tags: Comuter science, CUDA, Data mining, Java, Matrix multiplication, nVidia, nVidia Quadro K 2000, Package, Tesla K20, Tesla M2050

June 13, 2014 by hgpu

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

Prediction of Performance and Power Consumption of GPGPU Applications

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Descend: A Safe GPU Systems Programming Language

Redwood heterogeneous framework

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

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