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Lattice Quantum Chromodynamics on Intel Xeon Phi based supercomputers

Peter Labus

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Tags: Algorithms, HEP, Heterogeneous systems, Intel Xeon Phi, OpenMP, Physics, QCD, Thesis

April 26, 2017 by hgpu

Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: programming productivity, performance, and energy consumption

Suejb Memeti, Lu Li, Sabri Pllana, Joanna Kolodziej, Christoph Kessler

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Tags: Benchmarking, Computer science, CUDA, Energy-efficient computing, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan X, OpenACC, OpenCL, OpenMP, Performance

April 20, 2017 by hgpu

Random Finite Set Based Bayesian Filtering with OpenCL in a Heterogeneous Platform

Biao Hu, Uzair Sharif, Rajat Koner, Guang Chen, Kai Huang, Feihu Zhang, Walter Stechele, Alois Knoll

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Tags: AMD FirePro W9100, ATI, Bayesian, Benchmarking, Computer science, Filtering, Heterogeneous systems, OpenCL, Package

April 17, 2017 by hgpu

Investigation of heterogeneous computing through novel parallel programming platforms

Andrei-Alexandru Dafinoiu

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Tags: ARM, ATI, ATI Radeon HD 6450, ATI Radeon HD 6570, Computer science, FPGA, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 750 Ti, OpenCL, SoC, Thesis

April 17, 2017 by hgpu

Machine Learning from Streaming Data in Heterogeneous Computing Environments

Yusuf Guven Toprakkiran

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Tags: Algorithms, AMD Radeon R9 Fury, ATI, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 980, OpenCL, Thesis

April 11, 2017 by hgpu

A modular GPU raytracer using OpenCL for non-interactive graphics

Henrique Nunes Jung, Vinicius Jurinic Cassol

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenCL, Package, Raytracing, Rendering

April 11, 2017 by hgpu

Load Balancing for Constraint Solving with GPUs

Pedro Roque, Vasco Pedro, Salvador Abreu

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Tags: ATI, ATI Radeon HD 7990, Computer science, Heterogeneous systems, Intel Xeon Phi, load balancing, nVidia, nVidia GeForce GTX 980 M, OpenCL, Tesla K20

April 7, 2017 by hgpu

Merge or Separate? Multi-job Scheduling for OpenCL Kernels on CPU/GPU Platforms

Yuan Wen, Michael F.P. O'Boyle

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Tags: AMD Radeon HD 7970, ATI, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 780, OpenCL

April 3, 2017 by hgpu

Chai: Collaborative Heterogeneous Applications for Integrated-architectures

Juan Gomez-Luna, Izzat El Hajj, Li-Wen Chang, Victor Garcia-Flores, Simon Garcia de Gonzalo, Thomas B. Jablin, Antonio J. Pena, Wen-mei Hwu

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL

April 3, 2017 by hgpu

Optimization of Lattice Boltzmann Simulations on Heterogeneous Computers

E. Calore, A. Gabbana, S. F. Schifano, R. Tripiccione

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Tags: AMD FirePro W9100, ATI, Fluid dynamics, GPU cluster, Heterogeneous systems, Intel Xeon Phi, Lattice Boltzmann model, MPI, nVidia, OpenACC, OpenMP, Performance, performance portability, Tesla K80

March 20, 2017 by hgpu

Profiling High Level Heterogeneous Programs: Using the SPOC GPGPU framework for OCaml

Mathias Bourgoin, Emmanuel Chailloux, Anastasios Doumoulakis

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Package, Performance

February 18, 2017 by hgpu

gearshifft – The FFT Benchmark Suite for Heterogeneous Platforms

Peter Steinbach, Matthias Werner

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Tags: Algorithms, Benchmarking, Computer science, FFT, Heterogeneous systems, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, Tesla K20, Tesla K80, Tesla P100

February 10, 2017 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

Lattice Quantum Chromodynamics on Intel Xeon Phi based supercomputers

Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: programming productivity, performance, and energy consumption

Random Finite Set Based Bayesian Filtering with OpenCL in a Heterogeneous Platform

Investigation of heterogeneous computing through novel parallel programming platforms

Machine Learning from Streaming Data in Heterogeneous Computing Environments

A modular GPU raytracer using OpenCL for non-interactive graphics

Load Balancing for Constraint Solving with GPUs

Merge or Separate? Multi-job Scheduling for OpenCL Kernels on CPU/GPU Platforms

Chai: Collaborative Heterogeneous Applications for Integrated-architectures

Optimization of Lattice Boltzmann Simulations on Heterogeneous Computers

Profiling High Level Heterogeneous Programs: Using the SPOC GPGPU framework for OCaml

gearshifft – The FFT Benchmark Suite for Heterogeneous Platforms

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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