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Performance Analysis and Automatic Tuning of Hash Aggregation on GPUs

Viktor Rosenfeld, Sebastian Bress, Steffen Zeuch, Tilmann Rabl, Volker Markl

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Tags: Algorithms, AMD Radeon R9 Fury, ATI, Computer science, Hashing, nVidia, nVidia GeForce GXT 1080, nVidia GeForce GXT 980, OpenCL, Performance, Tesla K40, Tesla V100

June 16, 2019 by hgpu

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Shamrock: Multi-GPU hydrodynamics for astrophysics

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

LLMPerf: GPU Performance Modeling meets Large Language Models

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Celerity Runtime: High-level C++ for Accelerator Clusters

Concurrent Scheduling of High-Level Parallel Programs on Multi-GPU Systems

wgpy: WebGL accelerated numpy-compatible array library for web browser

WgPy: GPU-accelerated NumPy-like array library for web browsers

Microbenchmarking OpenMP target offload with Catch2

A Microbenchmark Framework for Performance Evaluation of OpenMP Target Offloading

SUperman: Highly Efficient Permanent Computation Library

SUperman: Efficient Permanent Computation on GPUs

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

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