high performance computing on graphics processing units: hgpu.org

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Model-Driven Tile Size Selection for DOACROSS Loops on GPUs

Peng Di, Jingling Xue

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Tags: Computer science, CUDA, nVidia, Performance, Programming techniques, Tesla C1060

February 20, 2012 by hgpu

Cross Teaching Parallelism and Ray Tracing: A Project-based Approach to Teaching Applied Parallel Computing

Chris Lupo, Zoe J. Wood, Christine Victorino

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Tags: Computer science, CUDA, Education, nVidia, nVidia GeForce GTX 470, Performance, Programming techniques, Raytracing, Rendering

February 14, 2012 by hgpu

Automatic Performance Optimization in ViennaCL for GPUs

Karl Rupp, Josef Weinbub, Florian Rudolf

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Source codes

Tags: Algorithms, ATI, ATI Radeon HD 5850, Computer science, Linear Algebra, nVidia, nVidia GeForce 8500 GT, nVidia GeForce GTX 470, OpenCL, Optimization, Package, Performance, Programming techniques

February 10, 2012 by hgpu

The Boat Hull Model: Adapting the Roofline Model to Enable Performance Prediction for Parallel Computing

Cedric Nugteren, Henk Corporaal

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, Performance

February 9, 2012 by hgpu

The PEPPHER Approach to Programmability and Performance Portability for Heterogeneous many-core Architectures

Siegfried Benkner, Sabri Pllana, Jesper Larsson Traff, Philippas Tsigas, Andrew Richards, Raymond Namyst, Beverly Bachmayer, Christoph Kessler, David Moloney, Peter Sanders

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Optimization, Performance, Tesla C1060

February 8, 2012 by hgpu

GMP implementation on CUDA – A Backward Compatible Design With Performance Tuning

Hao Jun Liu, Chu Tong

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Tags: Algorithms, Computer science, CUDA, Elementary functions, nVidia, nVidia GeForce GTX 280, Performance, Security

February 7, 2012 by hgpu

Enabling Traceability in MDE to Improve Performance of GPU Applications

Antonio Wendell De Oliveira Rodrigues, Vincent Aranega, Anne Etien, Frederic Guyomarc'H, Jean-Luc Dekeyser

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Tags: Algorithms, Computer science, nVidia, OpenCL, Performance, Programming techniques, Tesla S1070

February 7, 2012 by hgpu

Towards a Tunable Multi-Backend Skeleton Programming Framework for Multi-GPU Systems

Johan Enmyren, Usman Dastgeer, Christoph W. Kessler

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Tags: Code generation, Computer science, CUDA, Data parallelism, MapReduce, nVidia, nVidia GeForce GTS 250, OpenCL, Performance, Tesla C1060

January 30, 2012 by hgpu

Scalable Parallel Minimum Spanning Forest Computation

Sadegh Nobari, Thanh-Tung Cao, Panagiotis Karras, Stephane Bressan

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Performance, Tesla S1070

January 25, 2012 by hgpu

TAP: A TLP-Aware Cache Management Policy for a CPU-GPU Heterogeneous Architecture

Jaekyu Lee, Hyesoon Kim

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Tags: Computer science, Heterogeneous systems, Memory, Performance

January 25, 2012 by hgpu

Dynamically Managed Data for CPU-GPU Architectures

Thomas B. Jablin, James A. Jablin, Prakash Prabhu, Feng Liu, David I. August

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Tags: Computer science, CUDA, Data Structures and Algorithms, Memory model, nVidia, nVidia GeForce GTX 480, Performance

January 19, 2012 by hgpu

FPGA Based Acceleration of Decimal Operations

Alberto Nannarelli

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Tags: Computer science, Finance, FPGA, Performance

January 16, 2012 by hgpu

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

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

Can Large Language Models Predict Parallel Code Performance?

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high performance computing on graphics processing units: hgpu.org

Model-Driven Tile Size Selection for DOACROSS Loops on GPUs

Cross Teaching Parallelism and Ray Tracing: A Project-based Approach to Teaching Applied Parallel Computing

Automatic Performance Optimization in ViennaCL for GPUs

The Boat Hull Model: Adapting the Roofline Model to Enable Performance Prediction for Parallel Computing

The PEPPHER Approach to Programmability and Performance Portability for Heterogeneous many-core Architectures

GMP implementation on CUDA – A Backward Compatible Design With Performance Tuning

Enabling Traceability in MDE to Improve Performance of GPU Applications

Towards a Tunable Multi-Backend Skeleton Programming Framework for Multi-GPU Systems

Scalable Parallel Minimum Spanning Forest Computation

TAP: A TLP-Aware Cache Management Policy for a CPU-GPU Heterogeneous Architecture

Dynamically Managed Data for CPU-GPU Architectures

FPGA Based Acceleration of Decimal Operations

Recent source codes

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

microSYCL: SYCL micro-benchmarks repository

XaaS containers

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

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

Can Large Language Models Predict Parallel Code Performance?

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