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Towards shared memory consistency models for GPUs

Tyler Sorensen

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Tags: Computer science, CUDA, Memory model, nVidia, Performance, PTX, Thesis

June 4, 2013 by hgpu

Fast Makespan Estimation for GPU Threads on a Single Streaming Multiprocessor

Kostiantyn Berezovskyi, Konstantinos Bletsas, Stefan M. Petters

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Tags: Computer science, CUDA, Metaheuristics, nVidia, nVidia GeForce GTX 680, PTX, Timing analysis

April 22, 2013 by hgpu

CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions

Yongchao Liu, Adrianto Wirawan, Bertil Schmidt

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 690, Package, PTX, Sequence alignment, Smith-Waterman algorithm

April 10, 2013 by hgpu

Kernelet: High-Throughput GPU Kernel Executions with Dynamic Slicing and Scheduling

Jianlong Zhong, Bingsheng He

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, PTX, Task scheduling, Tesla C2050

March 23, 2013 by hgpu

Just-in-time Acceleration of JavaScript

Uday Pitambare, Arun Chauhan, Saurabh Malviya

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Tags: Computer science, Java, nVidia, Performance, PTX, Tesla C1060

March 12, 2013 by hgpu

Software Reliability Enhancements for GPU Applications

Si Li, Naila Farooqui, Sudhakar Yalamanchili

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 660 Ti, PTX

February 2, 2013 by hgpu

Scaling CUDA for Distributed Heterogeneous Processors

Siu Kwan Lam

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Tags: Computer science, CUDA, Heterogeneous systems, Memory model, MPI, nVidia, Package, PTX, Python, Thesis

July 20, 2012 by hgpu

Analyzing the CUDA Applications with its Latency and Bandwidth Tolerance

V.N. Pistulkar, C.A. Uttarwar

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Tags: Computer science, CUDA, GPU cluster, nVidia, PTX, Tesla C1060

July 5, 2012 by hgpu

Parallel Cryptanalysis

Ruben Falko Niederhagen

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Tags: Algorithms, Computer science, CUDA, MPI, nVidia, nVidia GeForce GTX 295, PTX, Security, Thesis

May 12, 2012 by hgpu

Usable assembly language for GPUs: a success story

Daniel J. Bernstein, Hsieh-Chung Chen, Chen-Mou Cheng, Tanja Lange, Ruben Niederhagen, Peter Schwabe, Bo-Yin Yang

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Performance, PTX, Security

March 18, 2012 by hgpu

An Energy Consumption Model for GPU Computing at Instruction Level

Haifeng Wang, Qingkui Chen

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Tags: Code generation, Computer science, Energy-efficient computing, nVidia, nVidia GeForce GTX 280, PTX

February 27, 2012 by hgpu

Symbolic Testing of OpenCL Code

Peter Collingbourne, Cristian Cadar, Paul H. J. Kelly

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Tags: Benchmarking, Code generation, Computer science, nVidia, OpenCL, Package, PTX, Tesla C1060

February 8, 2012 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

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

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

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high performance computing on graphics processing units: hgpu.org

Towards shared memory consistency models for GPUs

Fast Makespan Estimation for GPU Threads on a Single Streaming Multiprocessor

CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions

Kernelet: High-Throughput GPU Kernel Executions with Dynamic Slicing and Scheduling

Just-in-time Acceleration of JavaScript

Software Reliability Enhancements for GPU Applications

Scaling CUDA for Distributed Heterogeneous Processors

Analyzing the CUDA Applications with its Latency and Bandwidth Tolerance

Parallel Cryptanalysis

Usable assembly language for GPUs: a success story

An Energy Consumption Model for GPU Computing at Instruction Level

Symbolic Testing of OpenCL Code

Recent source codes

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

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

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

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

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

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