high performance computing on graphics processing units: hgpu.org

hgpu.org » Heterogeneous systems

A CUDA Back-End for the Equelle Compiler

Havard Heitlo Holm

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Tags: Compilers, Computer science, CUDA, Differential equations, Finite volume method, Heterogeneous systems, nVidia, nVidia Quadro NVS 5200 M, Package, Partial differential equations, PDEs, Tesla K40, Thesis

September 8, 2014 by hgpu

Dymaxion++: A Directive-based API to Optimize Data Layout and Memory Mapping for Heterogeneous Systems

Shuai Che, Jiayuan Meng, Kevin Skadron

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Tags: ATI, ATI Radeon HD 7950, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL

September 5, 2014 by hgpu

A uniform approach for programming distributed heterogeneous computing systems

Ivan Grasso, Simone Pellegrini, Biagio Cosenza, Thomas Fahringer

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Tags: AMD FirePro W9000, ATI, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Tesla K20

September 4, 2014 by hgpu

Heterogeneous Computing on Mixed Unstructured Grids with PyFR

F. D. Witherden, B. C. Vermeire, P. E. Vincent

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Tags: AMD FirePro W9100, ATI, CUDA, Fluid dynamics, Heterogeneous systems, nVidia, OpenCL, Package, Tesla K40

September 2, 2014 by hgpu

Fast reconstruction of 3D volumes from 2D CT projection data with GPUs

M. Leeser, S. Mukherjee, J. Brock

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Tags: AMD, AMD Radeon HD 7970, Computed tomography, CT, CUDA, Heterogeneous systems, Medicine, nVidia, OpenCL, Tesla C2075

September 1, 2014 by hgpu

Optimization of Data-Parallel Scientific Applications on Highly Heterogeneous Modern HPC Platforms

Ziming Zhong

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, Numerical simulation, nVidia, Tesla S2050, Thesis

August 27, 2014 by hgpu

Estimating GPU Speedups for Programs Without Writing a Single Line of GPU Code

Newsha Ardalani, Karthikeyan Sankaralingam, Xiaojin Zhu

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 660 Ti, OpenCL, Performance, Tesla K20

August 23, 2014 by hgpu

High Level Programming for Heterogeneous Architectures

Oren Segal, Martin Margala, Sai Rahul Chalamalasetti, Mitch Wright

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Tags: Computer science, FPGA, Hadoop, Heterogeneous systems, Java, MapReduce, OpenCL, Performance

August 23, 2014 by hgpu

A Domain Specific Approach to Heterogeneous Computing: From Availability to Accessibility

Gordon Inggs, David Thomas, Wayne Luk

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Tags: Computational finance, Finance, FPGA, Heterogeneous systems, OpenCL, Option pricing, Package, Python

August 23, 2014 by hgpu

Ocelot/HyPE: Optimized Data Processing on Heterogeneous Hardware

Sebastian Bress, Max Heimel, Michael Saecker, Bastian Kocher, Volker Markl, Gunter Saake

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Tags: Computer science, Heterogeneous systems, OpenCL

August 18, 2014 by hgpu

MPI-GIS: New Parallel Overlay Algorithm and System Prototype

Satish Puri, Sushil K. Prasad

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Tags: Algorithms, Computer science, CUDA, Electronic design automation, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX 780

August 11, 2014 by hgpu

Vivaldi: A Domain-Specific Language for Volume Processing and Visualization on Distributed Heterogeneous Systems

Hyungsuk Choi, Woohyuk Choi, Tran Minh Quan, David G. C. Hildebrand, Hanspeter Pfister, Senior Member, Won-Ki Jeong

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Tags: CUDA, GPU cluster, Heterogeneous systems, Image processing, nVidia, Python, Rendering, Tesla M2090, Visualization

August 9, 2014 by hgpu

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

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high performance computing on graphics processing units: hgpu.org

A CUDA Back-End for the Equelle Compiler

Dymaxion++: A Directive-based API to Optimize Data Layout and Memory Mapping for Heterogeneous Systems

A uniform approach for programming distributed heterogeneous computing systems

Heterogeneous Computing on Mixed Unstructured Grids with PyFR

Fast reconstruction of 3D volumes from 2D CT projection data with GPUs

Optimization of Data-Parallel Scientific Applications on Highly Heterogeneous Modern HPC Platforms

Estimating GPU Speedups for Programs Without Writing a Single Line of GPU Code

High Level Programming for Heterogeneous Architectures

A Domain Specific Approach to Heterogeneous Computing: From Availability to Accessibility

Ocelot/HyPE: Optimized Data Processing on Heterogeneous Hardware

MPI-GIS: New Parallel Overlay Algorithm and System Prototype

Vivaldi: A Domain-Specific Language for Volume Processing and Visualization on Distributed Heterogeneous Systems

Recent source codes

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

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