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Three Contributions to the Theory and Practice of Optimizing Compilers

Linxiao Wang

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Tags: Compilers, Computer science, CUDA, LLVM, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 760 M, Package, Thesis

December 4, 2022 by hgpu

Fast Fourier Transforms over Prime Fields of Large Characteristic and their Implementation on Graphics Processing Units

Davood Mohajerani

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Tags: Algorithms, Computer science, CUDA, FFT, nVidia, nVidia GeForce GTX 760 M, Security, Thesis

February 5, 2017 by hgpu

Implementation of a PIC simulation using WebGL

Leonard Weydemann

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Tags: Computer science, GLSL, Java, nVidia, nVidia GeForce GT 310, nVidia GeForce GTX 760 M, OpenGL, Particle-in-cell methods, Thesis

May 3, 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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