high performance computing on graphics processing units: hgpu.org

Pablo F. Zubieta Rico, Ludwig Schneider, Gustavo Perez-Lemus, Riccardo Alessandri, Siva Dasetty, Cintia A. Menéndez, Yiheng Wu, Yezhi Jin, Trung Nguyen, John Parker, Andrew L. Ferguson, Juan J. de Pablo

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Tags: Biology, Chemistry, Computational Physics, CUDA, Molecular dynamics, nVidia, Package, Physics, Python, Tesla V100

January 22, 2023 by hgpu

A Programming Model for GPU Load Balancing

Muhammad Osama, Serban D. Porumbescu, John D. Owens

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Tags: Algorithms, Computer science, CUDA, load balancing, nVidia, Package, Tesla V100

January 15, 2023 by hgpu

Improving the scalability of modern applications by parallel multi-core and many-core programming

Alessandro Borione

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, nVidia GeForce RTX 3070, OpenCL, Thesis

January 15, 2023 by hgpu

Distributed Calculations with Algorithmic Skeletons for Heterogeneous Computing Environments

Nina Herrmann, Herbert Kuchen

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeFroce RTX 2080 Ti, nVidia Quadro K620

January 15, 2023 by hgpu

Arax: a runtime framework for decoupling applications from heterogeneous accelerators

Manos Pavlidakis, Stelios Mavridis, Antony Chazapis, Giorgos Vasiliadis, Angelos Bilas

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce RTX 2080, OpenCL, Package

January 8, 2023 by hgpu

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

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

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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high performance computing on graphics processing units: hgpu.org

Evaluation of Rust for GPGPU high-performance computing

CMLCompiler: A Unified Compiler for Classical Machine Learning

Revisiting Query Performance in GPU Database Systems

A Symbolic Emulator for Shuffle Synthesis on the NVIDIA PTX Code

SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs

PIGEON: Optimizing CUDA Code Generator for End-to-End Training and Inference of Relational Graph Neural Networks

AutoDDL: Automatic Distributed Deep Learning with Asymptotically Optimal Communication

PySAGES: flexible, advanced sampling methods accelerated with GPUs

A Programming Model for GPU Load Balancing

Improving the scalability of modern applications by parallel multi-core and many-core programming

Distributed Calculations with Algorithmic Skeletons for Heterogeneous Computing Environments

Arax: a runtime framework for decoupling applications from heterogeneous accelerators

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

AutoDock for GPUs and other accelerators

spotPython: Sequential Parameter Optimization in Python

Test benchmarks for CELERITY

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

Beehive SPIR-V Toolkit

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Prediction of Performance and Power Consumption of GPGPU Applications

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