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The Optimization of Algorithms in the Process of Temporal Data Mining Using the Compute Unified Device Architecture

Alexandru Pirjan

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Tags: Algorithms, Computer science, CUDA, Data mining, MapReduce, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Review

November 24, 2011 by hgpu

Automated architecture-aware mapping of streaming applications onto GPUs

Andrei Hagiescu, Huynh Phung Huynh, Weng-Fai Wong, Rick Siow Mong Goh

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, OpenCL, Performance, Task scheduling, Tesla S1070, Tesla S2050

November 23, 2011 by hgpu

Real-World Constraints of GPUs in Real-Time Systems

Glenn A. Elliott, James H. Anderson

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Tags: Computer science, Overview, Performance

November 23, 2011 by hgpu

Soren: Adaptive MapReduce for Programmable GPUs

Reza Mokhtari, Amin Abbasi, Farshad Khunjush, Reza Azimi

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Tags: Computer science, CUDA, MapReduce, nVidia, nVidia GeForce GTX 480

November 23, 2011 by hgpu

Towards solving the Table Maker’s Dilemma on GPU

Pierre Fortin, Mourad Gouicem, Stef Graillat

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Tags: Algorithms, Computer science, CUDA, Elementary functions, nVidia, Tesla C2070

November 23, 2011 by hgpu

Building-Blocks for Performance Oriented DSLs

Tiark Rompf, Arvind K. Sujeeth, HyoukJoong Lee, Kevin J. Brown, Hassan Chafi, Martin Odersky, Kunle Olukotun

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Programming Languages, Tesla C2050

November 23, 2011 by hgpu

TEG: GPU Performance Estimation Using a Timing Model

Junjie Lai, Andre Seznec

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance

November 23, 2011 by hgpu

Dynamic adaptation and distribution of binaries to heterogeneous architectures

Espen Angell Kristiansen

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Source codes

Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, nVidia GeForce GT 220, nVidia GeForce GTX 280, OpenCL, Package, Playstation, Thesis

November 22, 2011 by hgpu

Dynamic Heterogeneous Scheduling Decisions Using Historical Runtime Data

Chris Gregg, Michael Boyer, Kim Hazelwood, Kevin Skadron

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Tags: ATI, ATI Radeon HD 5870, Computer science, Heterogeneous systems, OpenCL, Performance, Task scheduling

November 22, 2011 by hgpu

Parallelizing Multicore Cache Simulations using Heterogeneous Computing on General Purpose and Graphics Processors

Nikolaos Strikos, Georgios Keramidas, Stefanos Kaxiras

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Tags: Computer science, CUDA, Heterogeneous systems, Memory, nVidia, nVidia GeForce 8800 GTX

November 22, 2011 by hgpu

GPU-based Multi-start Local Search Algorithms

The Van Luong, Nouredine Melab, El-Ghazali Talbi

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Tags: Algorithms, Computer science, CUDA, Metaheuristics, nVidia, nVidia GeForce GTX 280, Optimization, Search

November 22, 2011 by hgpu

Experiences with Achieving Portability across Heterogeneous Architectures

Lukasz G. Szafaryn, Todd Gamblin, Bronis R. de Supinski, Kevin Skadron

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 460, OpenMP, Performance

November 22, 2011 by hgpu

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

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

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

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

The Optimization of Algorithms in the Process of Temporal Data Mining Using the Compute Unified Device Architecture

Automated architecture-aware mapping of streaming applications onto GPUs

Real-World Constraints of GPUs in Real-Time Systems

Soren: Adaptive MapReduce for Programmable GPUs

Towards solving the Table Maker’s Dilemma on GPU

Building-Blocks for Performance Oriented DSLs

TEG: GPU Performance Estimation Using a Timing Model

Dynamic adaptation and distribution of binaries to heterogeneous architectures

Dynamic Heterogeneous Scheduling Decisions Using Historical Runtime Data

Parallelizing Multicore Cache Simulations using Heterogeneous Computing on General Purpose and Graphics Processors

GPU-based Multi-start Local Search Algorithms

Experiences with Achieving Portability across Heterogeneous Architectures

Recent source codes

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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

hls4ml: Machine learning on FPGAs using HLS

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

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

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

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