high performance computing on graphics processing units: hgpu.org

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Understanding the Impact of Hybrid Programming on Software Energy Efficiency

Donna LaKomski

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Tags: Computer science, CUDA, Heterogeneous systems, Hybrid computing, Intel Xeon Phi, nVidia, OpenCL, Tesla K20, Thesis

September 3, 2016 by hgpu

Deep Learning on FPGAs

Griffin James Lacey

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Tags: Computer science, Deep learning, FPGA, nVidia, nVidia Quadro K600, OpenCL, Thesis

August 31, 2016 by hgpu

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

Yue Hu

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Tags: Code generation, Computer science, CUDA, DSL, Fluid dynamics, Intel Xeon Phi, nVidia, OpenCL, Package, PDEs, PTX, Stencil computation, Tesla K20, Thesis

August 31, 2016 by hgpu

GPU-Acceleration of In-Memory Data Analytics

Evangelia Sitaridi

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, String matching, Tesla C2070, Tesla K40, Thesis

August 18, 2016 by hgpu

SkePU 2: Language Embedding and Compiler Support for Flexible and Type-Safe Skeleton Programming

August Ernstsson

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, OpenMP, Programming techniques, Tesla K20, Thesis

August 18, 2016 by hgpu

Programming Embedded Manycore: Refinement and Optimizing Compilation of a Parallel Action Language for Hierarchical State Machines

Ivan Llopard

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Tags: Compilers, Computer science, OpenCL, Thesis

August 4, 2016 by hgpu

Automatic Loop Partitioning for Heterogeneous Systems

Alexander Baier

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan Black, Performance, PTX, Thesis

August 1, 2016 by hgpu

Unified system of code transformation and execution for heterogeneous multi-core architectures

Pei Li

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia Quadro FX 5800, OpenCL, Thesis

August 1, 2016 by hgpu

Towards Predictable Real-Time Performance on Multi-Core Platforms

Hyoseung Kim

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Tags: Computer science, Performance, Sensing, Thesis, Virtualization

July 30, 2016 by hgpu

Particle-in-cell algorithms for plasma simulations on heterogeneous architectures

Xavier Saez Pous

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Tags: ARM, Computational Physics, Computer science, Heterogeneous systems, MPI, OpenCL, Particle-in-cell methods, Plasma physics, Thesis

July 30, 2016 by hgpu

An Autotuning Framework for Intel Xeon Phi Platforms

Lefteris Christoforidis

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Tags: Computer science, Intel Xeon Phi, Package, Performance, Python, Thesis

July 30, 2016 by hgpu

Many-Core Algorithms for Combinatorial Optimization

Francesco Strappaveccia

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Tags: Algorithms, Computer science, CUDA, MPI, nVidia, nVidia GeForce GTX 570, nVidia GeForce GTX 770, nVidia GeForce GTX Titan, OpenMP, Optimization, Path problems, Thesis

July 28, 2016 by hgpu

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

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

BoltzGen:Toward Universal Binder Design

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

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

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

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high performance computing on graphics processing units: hgpu.org

Understanding the Impact of Hybrid Programming on Software Energy Efficiency

Deep Learning on FPGAs

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

GPU-Acceleration of In-Memory Data Analytics

SkePU 2: Language Embedding and Compiler Support for Flexible and Type-Safe Skeleton Programming

Programming Embedded Manycore: Refinement and Optimizing Compilation of a Parallel Action Language for Hierarchical State Machines

Automatic Loop Partitioning for Heterogeneous Systems

Unified system of code transformation and execution for heterogeneous multi-core architectures

Towards Predictable Real-Time Performance on Multi-Core Platforms

Particle-in-cell algorithms for plasma simulations on heterogeneous architectures

An Autotuning Framework for Intel Xeon Phi Platforms

Many-Core Algorithms for Combinatorial Optimization

Recent source codes

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

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

BoltzGen:Toward Universal Binder Design

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

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

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