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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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

mt4g: Memory Topology 4 GPUs

MT4G: A Tool for Reliable Auto-Discovery of NVIDIA and AMD GPU Compute and Memory Topologies

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

A High-Throughput GPU Framework for Adaptive Lossless Compression of Floating-Point Data

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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