high performance computing on graphics processing units: hgpu.org

Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Hongxu Yin, Fei Sun, Yanghan Wang, Marat Dukhan, Yunqing Hu, Yiming Wu, Yangqing Jia, Peter Vajda, Matt Uyttendaele, Niraj K. Jha

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Tags: Bayesian, Computer science, CUDA, Deep learning, DSP, Neural networks, nVidia, nVidia GeForce GTX 1060

December 30, 2018 by hgpu

Low-power System-on-Chip Processors for Energy Efficient High Performance Computing: The Texas Instruments Keystone II

Gaurav Mitra

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Tags: ARM, Benchmarking, Computer science, DSP, Heterogeneous systems, Matrix multiplication, nVidia, nVidia Jetson TK1, nVidia Tegra TX1, OpenCL, SoC, Thesis

November 12, 2017 by hgpu

Synthesis of Embedded Software using Dataflow Schedule Graphs

Abhay Raina

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Tags: Computer science, DSP, nVidia, nVidia GeForce GTX 680, OpenCL, Signal processing, Thesis

July 2, 2017 by hgpu

A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA

Xinyu Zhang, Srinjoy Das, Ojash Neopane, Ken Kreutz-Delgado

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Tags: Algorithms, Computer science, Computer vision, Deep learning, DSP, FPGA, Neural networks

May 11, 2017 by hgpu

Resource-Aware Just-in-Time OpenCL Compiler for Coarse-Grained FPGA Overlays

Abhishek Kumar Jain, Douglas L. Maskell, Suhaib A. Fahmy

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Tags: ARM, Computer science, DSP, FPGA, Heterogeneous systems, OpenCL

May 11, 2017 by hgpu

Introduction to the Special Issue on Digital Signal Processing in Radio Astronomy

Danny C. Price, Jonathon Kocz, Matthew Bailes, Lincoln J. Greenhill

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Tags: Astrophysics, CUDA, DSP, FPGA, nVidia, Overview, Signal processing

February 7, 2017 by hgpu

GCN Inference Acceleration HLS

GCN Inference Acceleration using High-Level Synthesis

Climate Machine: an Earth System Model that automatically learns from data

Large-eddy simulations with ClimateMachine: a new open-source code for atmospheric simulations on GPUs and CPUs

HLS Portability from Intel to Xilinx: A Case Study

HEXL: Intel Homomorphic Encryption Acceleration Library

Accelerating Encrypted Computing on Intel GPUs

Taskflow: A General-purpose Parallel and Heterogeneous Task Programming System

Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System

CompilerGym: a library performant reinforcement learning environments for compiler tasks

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

Thermal Safety and Real-Time Predictability on Heterogeneous Embedded SoC Platforms

Using Machine Learning to Estimate Utilization and Throughput for OpenCL-Based SpMV Implementation on an FPGA

Hardware Implementation and Quantization of Tiny-Yolo-v2 using OpenCL

Software Compilation Techniques for Heterogeneous Embedded Multi-Core Systems

Accelerating ternary quantized convolutional neural networks using OpenCL for FPGA

ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation

Low-power System-on-Chip Processors for Energy Efficient High Performance Computing: The Texas Instruments Keystone II

Synthesis of Embedded Software using Dataflow Schedule Graphs

A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA

Resource-Aware Just-in-Time OpenCL Compiler for Coarse-Grained FPGA Overlays

Introduction to the Special Issue on Digital Signal Processing in Radio Astronomy

Recent source codes

GCN Inference Acceleration HLS

Climate Machine: an Earth System Model that automatically learns from data

HLS Portability from Intel to Xilinx: A Case Study

HEXL: Intel Homomorphic Encryption Acceleration Library

Taskflow: A General-purpose Parallel and Heterogeneous Task Programming System

CompilerGym: a library performant reinforcement learning environments for compiler tasks

HPCToolkit

NVPTX-SPIR-V Translator

Taichi Lattice Boltmann

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

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