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DNA sequence alignment: An assignment for OpenMP, MPI, and CUDA/OpenCL

Arturo Gonzalez-Escribano, Diego García-Álvarez, Jesús Cámara

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Tags: Bioinformatics, Biology, CUDA, MPI, nVidia, nVidia A100, nVidia GeForce GTX Titan X, nVidia GeForce RTX 4050, OpenCL, Optimization, Sequence alignment

September 15, 2024 by hgpu

Refining HPCToolkit for application performance analysis at exascale

Laksono Adhianto, Jonathon Anderson, Robert Matthew Barnett, Dragana Grbic, Vladimir Indic, Mark Krentel, Yumeng Liu, Srdan Milakovíc, Wileam Phan, John Mellor-Crumme

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Tags: AMD Radeon Instinct MI300A, ATI, Computer science, CUDA, HPC, MPI, OpenCL, Package, Performance

September 15, 2024 by hgpu

The VerCors Verifier: A Progress Report

Lukas Armborst, Pieter Bos, Lars B. van den Haak, Marieke Huisman, Robert Rubbens, Ömer Şakar, Philip Tasche

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Tags: Computer science, CUDA, OpenCL, OpenMP, Package, Performance, SYCL

August 18, 2024 by hgpu

Anatomizing Deep Learning Inference in Web Browsers

Qipeng Wang, Shiqi Jiang, Zhenpeng Chen, Xu Cao, Yuanchun Li, Aoyu Li, Yun Ma, Ting Cao, Xuanzhe Liu

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 980, nVidia GeForce RTX 2060, OpenCL, Package, Performance, TensorFlow

August 18, 2024 by hgpu

A Comparison of OpenCL, CUDA, and HIP as Compilation Targets for a Functional Array Language

Troels Henriksen

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Tags: AMD Radeon Instinct MI100, ATI, Code generation, Computer science, CUDA, HIP, nVidia, nVidia A100, OpenCL, Package, Programming Languages

July 28, 2024 by hgpu

The Impact of Modern Consumer GPUs on Commonly Used Secure Password Standards

Hani Khader

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Tags: AMD Radeon RX 6800, ATI, Computer science, CUDA, nVidia, nVidia GeForce RTX 4090, OpenCL, Security, Thesis

July 14, 2024 by hgpu

Towards Unified Analysis of GPU Consistency

Haining Tong, Natalia Gavrilenko, Hernán Ponce de León, Keijo Heljanko

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Tags: Computer science, Memory, nVidia, OpenCL, Package, PTX, Vulkan

July 7, 2024 by hgpu

Automatic Code Rewriting for Performance Portability

Alister Johnson

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Tags: Compilers, Computer science, HIP, OpenCL, OpenMP, performance portability, SYCL, Thesis

July 7, 2024 by hgpu

CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization

Jacob O. Tørring, Carl Hvarfner, Luigi Nardi, Magnus Själander

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Tags: Benchmarking, Compilers, Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan V, nVidia Titan RTX, OpenCL, Package

June 30, 2024 by hgpu

A Survey of General-purpose Polyhedral Compilers

Arun Thangamani, Vincent Loechner, Stéphane Genaud

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia A100, OpenCL, Package, survey

June 30, 2024 by hgpu

GPU Parallelization of Astronomical Image Subtraction

Gustav Arneving, Hugo Wilhelmsson

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Tags: Astrophysics, nVidia, nVidia GeForce GTX 1050, OpenCL, OpenMP, Physics, Thesis

June 23, 2024 by hgpu

Fast and Practical Strassen’s Matrix Multiplication using FPGAs

Afzal Ahmad, Linfeng Du, Wei Zhang

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Tags: BLAS, Computer science, FPGA, GEMM, Linear Algebra, Machine learning, Matrix multiplication, OpenCL, Package

June 9, 2024 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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high performance computing on graphics processing units: hgpu.org

DNA sequence alignment: An assignment for OpenMP, MPI, and CUDA/OpenCL

Refining HPCToolkit for application performance analysis at exascale

The VerCors Verifier: A Progress Report

Anatomizing Deep Learning Inference in Web Browsers

A Comparison of OpenCL, CUDA, and HIP as Compilation Targets for a Functional Array Language

The Impact of Modern Consumer GPUs on Commonly Used Secure Password Standards

Towards Unified Analysis of GPU Consistency

Automatic Code Rewriting for Performance Portability

CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization

A Survey of General-purpose Polyhedral Compilers

GPU Parallelization of Astronomical Image Subtraction

Fast and Practical Strassen’s Matrix Multiplication using FPGAs

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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