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

Juan Fumero, György Rethy, Athanasios Stratikopoulos, Nikos Foutris, Christos Kotselidis

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Tags: Code generation, Computer science, Heterogeneous systems, Java, OpenCL, Package

May 21, 2023 by hgpu

Towards Alignment of Parallelism in SYCL and ISO C++

Simon John Pennycook, Ben Ashbaugh, James Brodman, Michael Kinsner, Steffen Larsen, Greg Lueck, Roland Schulz, Michael Voss

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Tags: Computer science, OpenCL, Performance, SYCL

May 14, 2023 by hgpu

Performance Optimization using Multimodal Modeling and Heterogeneous GNN

Akash Dutta, Jordi Alcaraz, Ali TehraniJamsaz, Eduardo Cesar, Anna Sikora, Ali Jannesari

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Tags: AMD Radeon HD 7970, ATI, Benchmarking, Computer science, Deep learning, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX 970, OpenCL, OpenMP

May 14, 2023 by hgpu

Descend: A Safe GPU Systems Programming Language

Bastian Köpcke, Sergei Gorlatch, Michel Steuwer

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Tags: Computer science, CUDA, nVidia, OpenCL, Package, Programming Languages, Tesla P100

May 14, 2023 by hgpu

Simple and efficient GPU accelerated topology optimisation: Codes and applications

Erik A. Träff, Anton Rydahl, Sven Karlsson, Ole Sigmund, Niels Aage

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Tags: Computer science, CUDA, nVidia, nVidia A100, OpenCL, OpenMP, Package, Topology optimization problem

April 23, 2023 by hgpu

Kernel Tuning Toolkit

Filip Petrovič, Jiří Filipovič

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 680, nVidia GeForce GTX 750, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Performance, Python, Vulkan

April 16, 2023 by hgpu

Task parallelism-based architectures on FPGA to optimize the energy efficiency of AI at the edge

Rafael Gadea-Gironés, Jorge Fe, Jose M. Monzo

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Tags: Artificial intelligence, Computer science, Energy-efficient computing, FPGA, Neural networks, OpenCL

April 2, 2023 by hgpu

Challenges and Opportunities in C/C++ Source-To-Source Compilation

João Bispo, Nuno Paulino, Luís Miguel Sousa

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Tags: Compilers, Computer science, CUDA, nVidia, OpenCL, Package

March 19, 2023 by hgpu

BenchDirect: A Directed Language Model for Compiler Benchmarks

Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim Hazelwood, Ajitha Rajan, Hugh Leather

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Tags: Benchmarking, Compilers, Computer science, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

March 12, 2023 by hgpu

EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs

Manuel de Castro, Inmaculada Santamaria-Valenzuela, Yuri Torres, Arturo Gonzalez-Escribano, Diego R. Llanos

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Tags: AMD FirePro W9100, ATI, Computer science, Heterogeneous systems, Numerical simulation, nVidia, OpenCL, Package, Stencil computation, Tesla V100

February 12, 2023 by hgpu

Improving Performance of Hardware Accelerators by Optimizing Data Movement: A Bioinformatics Case Study

Peter Knoben, Nikolaos Alachiotis

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Tags: Bioinformatics, Biology, Computer science, FPGA, OpenCL, Package

February 12, 2023 by hgpu

Optimization of massive data applications on heterogeneous architectures

José Carlos Romero Moreno

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Tags: Computer science, FPGA, Heterogeneous systems, Intel, Intel UHD 630, OpenCL, Package, SYCL, Thesis

February 5, 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

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

Towards Alignment of Parallelism in SYCL and ISO C++

Performance Optimization using Multimodal Modeling and Heterogeneous GNN

Descend: A Safe GPU Systems Programming Language

Simple and efficient GPU accelerated topology optimisation: Codes and applications

Kernel Tuning Toolkit

Task parallelism-based architectures on FPGA to optimize the energy efficiency of AI at the edge

Challenges and Opportunities in C/C++ Source-To-Source Compilation

BenchDirect: A Directed Language Model for Compiler Benchmarks

EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs

Improving Performance of Hardware Accelerators by Optimizing Data Movement: A Bioinformatics Case Study

Optimization of massive data applications on heterogeneous architectures

Recent source codes

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

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

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Redwood heterogeneous framework

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