high performance computing on graphics processing units: hgpu.org

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SparkJNI: A Reference Design for a Heterogeneous Apache Spark Framework

Tudor Alexandru Voicu

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Tags: big data, Computer science, FPGA, Heterogeneous systems, Hybrid computing, Java, OpenCL, Spark, Thesis

June 1, 2017 by hgpu

Implementing Efficient, Portable Computations for Machine Learning

Matthew Walter Moskewicz

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Tags: Algorithms, AMD R9 Nano, ATI, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Thesis

May 24, 2017 by hgpu

GPUMap: A Transparently GPU-Accelerated Map Function

Ivan Pachev

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Tags: Computer science, CUDA, Distributed computing, Java, MapReduce, nVidia, nVidia GeForce GTX 980, Python, Thesis

May 22, 2017 by hgpu

Towards Enhancing Performance, Programmability, and Portability in Heterogeneous Computing

Konstantinos Krommydas

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Tags: ATI, ATI Radeon HD 6550, ATI Radeon HD 7660, ATI Radeon HD 7970, Code generation, Compilers, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Package, Performance, Tesla K20, Thesis

May 9, 2017 by hgpu

Acceleration of Deep Learning on FPGA

Huyuan Li

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Tags: CNN, Computer science, Deep learning, FPGA, Neural networks, nVidia, OpenCL, Tesla K40, Thesis

May 9, 2017 by hgpu

Automatic source code adaptation for heterogeneous platforms

Albert Saa-Garriga

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, OpenMP, Tesla M2050, Thesis

April 30, 2017 by hgpu

Developing a massive real-time crowd simulation framework on the GPU

Guillaume Payet

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Tags: Algorithms, Computer science, Crowd simulation, nVidia, nVidia GeForce GTX 850 M, OpenCL, Thesis

April 26, 2017 by hgpu

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

A Training Framework and Architectural Design for Distributed Deep Learning

Wei Wang

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan X, Package, Python, Thesis

April 26, 2017 by hgpu

Exploration of cyber-physical systems for GPGPU computer vision-based detection of biological viruses

Pascal Libuschewski

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Tags: Biology, Computer vision, Design space exploration, Microscopy, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 560 Ti, nVidia Quadro NVS 3100 M, OpenCL, Thesis, Visualization

April 20, 2017 by hgpu

Evaluation of GPU-based track-triggering for the CMS detector at CERN’s HL-LHC

Hannes Mohr

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Tags: Algorithms, ASIC, CUDA, FPGA, HEP, nVidia, Physics, Tesla K40, Tesla K80, Thesis

April 20, 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

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

Theses

SparkJNI: A Reference Design for a Heterogeneous Apache Spark Framework

Implementing Efficient, Portable Computations for Machine Learning

GPUMap: A Transparently GPU-Accelerated Map Function

Towards Enhancing Performance, Programmability, and Portability in Heterogeneous Computing

Acceleration of Deep Learning on FPGA

Automatic source code adaptation for heterogeneous platforms

Developing a massive real-time crowd simulation framework on the GPU

Lattice Quantum Chromodynamics on Intel Xeon Phi based supercomputers

A Training Framework and Architectural Design for Distributed Deep Learning

Exploration of cyber-physical systems for GPGPU computer vision-based detection of biological viruses

Evaluation of GPU-based track-triggering for the CMS detector at CERN’s HL-LHC

Investigation of heterogeneous computing through novel parallel programming platforms

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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