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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

DLIO Benchmark

DLIO: A Data-Centric Benchmark for Scientific Deep Learning Applications

kEDM: A Performance-portable Implementation of Empirical Dynamic Modeling using Kokkos

cuSZ: CUDA-Based Error-Bounded Lossy Compressor for Scientific Data

cuSZ(x): Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs

TornadoVM

Automatically Exploiting the Memory Hierarchy of GPUs through Just-in-Time Compilation

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high performance computing on graphics processing units: hgpu.org

Performance Analysis and Automatic Tuning of Hash Aggregation on GPUs

Recent source codes

DLIO Benchmark

kEDM: A Performance-portable Implementation of Empirical Dynamic Modeling using Kokkos

cuSZ: CUDA-Based Error-Bounded Lossy Compressor for Scientific Data

TornadoVM

Floyd-Warshall function of this implementation for Intel Xeon Phi Knights Landing

NPBench: A Benchmarking Suite for High-Performance NumPy

PeriPy - A High Performance OpenCL Peridynamics Package

PoCL: Portable Computing Language

Voxar MPS: Fluid simulation framework based on the MPS method

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Featured events

9th International Workshop on OpenCL and SYCL, 2021

The 18th International Conference on High Performance Computing & Simulation (HPCS), 2020

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

2nd International Conference on Frontiers of Intelligent Manufacturing and Automation (CFIMA’21), 2021

International Conference on Cyber Physical Systems and IoT (CPSIOT’20), 2020