high performance computing on graphics processing units: hgpu.org

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Matrix Computations and Optimization in Apache Spark

Reza Bosagh Zadeh, Xiangrui Meng, Alexander Ulanov, Burak Yavuz, Li Pu, Shivaram Venkataraman, Evan Sparks, Aaron Staple, Matei Zaharia

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, Package, Scala, Spark, Tesla M2050

September 3, 2016 by hgpu

Ultra-Fast Detection of Higher-Order Epistatic Interactions on GPUs

Daniel Junger, Christian Hundt, Jorge Gonzalez-Dominguez, Bertil Schmidt

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Tags: Algorithms, Bioinformatics, Clustering, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX Titan X

September 3, 2016 by hgpu

Fast 4D Sheared Filtering for Interactive Rendering of Distribution Effects

Ling-Qi Yan, Soham Uday Mehta, Ravi Ramamoorthi, Fredo Durand

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX Titan, Package, Raytracing, Rendering

September 3, 2016 by hgpu

DeepPy: Pythonic deep learning

Anders Boesen Lindbo Larsen

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Python

September 3, 2016 by hgpu

SafeGPU: Contract- and Library-Based GPGPU for Object-Oriented Languages

Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, nVidia Quadro K 2000 M, Package

August 31, 2016 by hgpu

Flux tubes at Finite Temperature

Pedro Bicudo, Nuno Cardoso, Marco Cardoso

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Tags: CUDA, High Energy Physics - Lattice, nVidia, Phase transition, Physics, QCD

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

Exploring Task Parallelism for Heterogeneous Systems Using Multicore Task Management API

Suyang Zhu, Sunita Chandrasekaran, Peng Sun, Barbara Chapman, Marcus Winter, Tobias Schuele

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson TK1

August 28, 2016 by hgpu

Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA

Dennis Bazow, Ulrich W. Heinz, Michael Strickland

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Tags: Algorithms, Computational Physics, CUDA, Fluid dynamics, High Energy Physics - Phenomenology, Intel Xeon Phi, Nuclear Theory, nVidia, nVidia GeForce GTX 560 M, nVidia GeForce GTX 980 Ti, nVidia GeForce GTX Titan Z, Physics

August 28, 2016 by hgpu

Fast Multidimensional Image Processing with OpenCL

Daniel Oliveira Dantas, Helton Danilo Passos Leal, Davy Oliveira Barros Sousa

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Tags: AMD Radeon R9 270X, ATI, CUDA, GLSL, Image processing, OpenCL, Package

August 23, 2016 by hgpu

MetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-core Clusters

Ahmed E. Helal, Paul Sathre, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Fluid dynamics, Heterogeneous systems, Intel Xeon Phi, MPI, nVidia, OpenCL, performance portability, Tesla K20

August 23, 2016 by hgpu

MAGMA Batched: A Batched BLAS Approach for Small Matrix Factorizations and Applications on GPUs

Tingxing Dong, Azzam Haidar, Piotr Luszczek, Stanimire Tomov, Ahmad Abdelfattah, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Factorization, Fluid dynamics, Linear Algebra, nVidia, Tesla K40

August 23, 2016 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

Matrix Computations and Optimization in Apache Spark

Ultra-Fast Detection of Higher-Order Epistatic Interactions on GPUs

Fast 4D Sheared Filtering for Interactive Rendering of Distribution Effects

DeepPy: Pythonic deep learning

SafeGPU: Contract- and Library-Based GPGPU for Object-Oriented Languages

Flux tubes at Finite Temperature

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

Exploring Task Parallelism for Heterogeneous Systems Using Multicore Task Management API

Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA

Fast Multidimensional Image Processing with OpenCL

MetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-core Clusters

MAGMA Batched: A Batched BLAS Approach for Small Matrix Factorizations and Applications on GPUs

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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