high performance computing on graphics processing units: hgpu.org

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The Accelerator Wall: Limits of Chip Specialization

Adi Fuchs, David Wentzlaff

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Tags: ASIC, Computer science, FPGA

June 2, 2019 by hgpu

Classify QCD phase transition with deep learning

Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stoecker, Xin-Nian Wang

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Tags: Deep learning, HEP, Machine learning, Neural networks, OpenCL, Package, Pattern recognition, Phase transition, Physics, QCD

June 2, 2019 by hgpu

A Development Platform for Embedded Domain-Specific Languages

Shigeru Chiba, YungYu Zhuang, Thanh-Chung Dao

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Tags: Computer science, CUDA, DSL, High-level Languages, Java, nVidia, Package

June 2, 2019 by hgpu

Heterogeneous Resource-Elastic Scheduling for CPU+FPGA Architectures

Anuj Vaishnav, Khoa Dang Pham, Dirk Koch

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Tags: Computer science, FPGA, Heterogeneous systems, OpenCL

June 2, 2019 by hgpu

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

Yunfei Teng, Wenbo Gao, Francois Chalus, Anna Choromanska, Donald Goldfarb, Adrian Weller

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080

June 2, 2019 by hgpu

Breadth-First Search using Dynamic Parallelism on the GPU

Dominik Todling

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Tags: Algorithms, Breadth-First Search, Computer science, CUDA, nVidia, Package

May 30, 2019 by hgpu

The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

Zhenheng Tang, Yuxin Wang, Qiang Wang, Xiaowen Chu

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 2080 Ti, Performance, Tesla P100, Tesla V100

May 30, 2019 by hgpu

Massively Parallel GPU Memory Compaction

Matthias Springer, Hidehiko Masuhara

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan XP, Performance

May 30, 2019 by hgpu

Multi-GPU Rendering with Vulkan API

Lars Olav Tolo

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Tags: AMD Radeon RX 460, ATI, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 970, Package, Thesis, Vulkan

May 26, 2019 by hgpu

Automatic generation of warp-level primitives and atomic instructions for fast and portable parallel reduction on GPUs

Simon Garcia De Gonzalo, Sitao Huang, Juan Gomez-Luna, Simon Hammond, Onur Mutlu, Wen-mei Hwu

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Performance, performance portability, Tesla K40, Tesla P100

May 26, 2019 by hgpu

Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel FPGAs

C. Jiang, D. Ojika, T. Kurth, Prabhat, S. Vallecorsa, B. Patel, H. Lam

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks

May 26, 2019 by hgpu

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

Murad Qasaimeh, Kristof Denolf, Jack Lo, Kees Vissers, Joseph Zambreno, Phillip H. Jones

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Tags: ARM, Benchmarking, Computer science, Computer vision, CUDA, FPGA, nVidia, nVidia Jetson TX2, OpenCV, Package

May 26, 2019 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

Applications

The Accelerator Wall: Limits of Chip Specialization

Classify QCD phase transition with deep learning

A Development Platform for Embedded Domain-Specific Languages

Heterogeneous Resource-Elastic Scheduling for CPU+FPGA Architectures

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

Breadth-First Search using Dynamic Parallelism on the GPU

The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

Massively Parallel GPU Memory Compaction

Multi-GPU Rendering with Vulkan API

Automatic generation of warp-level primitives and atomic instructions for fast and portable parallel reduction on GPUs

Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel FPGAs

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

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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