high performance computing on graphics processing units: hgpu.org

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

Symbolic Testing of OpenCL Code

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

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Source codes

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

February 8, 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

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

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

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high performance computing on graphics processing units: hgpu.org

Programming

Task Parallelism and Synchronization: An Overview of Explicit Parallel Programming Languages

High-throughput protein crystallization on the World Community Grid and the GPU

Characterizing and Evaluating a Key-value Store Application on Heterogeneous CPU-GPU Systems

Scalability of Self-organizing Maps on a GPU cluster using OpenCL and CUDA

Automatic Performance Optimization in ViennaCL for GPUs

Customizing Instruction Set Extensible Reconfigurable Processors using GPUs

Ensemble K-means on multi-core architectures

Implementing Molecular Dynamics on Hybrid High Performance Computers – Particle-Particle Particle-Mesh

Real-Time SAH BVH Construction for Ray Tracing Dynamic Scenes

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

Symbolic Testing of OpenCL Code

Enabling Traceability in MDE to Improve Performance of GPU Applications

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

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

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