high performance computing on graphics processing units: hgpu.org

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ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

Khoa Dang Pham, Anuj Vaishnav, Malte Vesper, Dirk Koch

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Tags: Computer science, FPGA, HLS, OpenCL, Package, Task scheduling

September 16, 2018 by hgpu

Spatial: A Language and Compiler for Application Accelerators

David Koeplinger, Matthew Feldman, Raghu Prabhakar, Yaqi Zhang, Stefan Hadjis, Ruben Fiszel, Tian Zhao, Luigi Nardi, Ardavan Pedram, Christos Kozyrakis, Kunle Olukotun

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Tags: Compilers, Computer science, Deep learning, DSL, FPGA, HLS, Machine learning, Package, Scala

July 21, 2018 by hgpu

A High-efficiency FPGA-based Accelerator for Convolutional Neural Networks using Winograd Algorithm

Y. Huang, J. Shen, Z. Wang, M. Wen, C. Zhang

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Tags: Computational Complexity, Computer science, Computer vision, Deep learning, FPGA, HLS, Neural networks

June 9, 2018 by hgpu

Transformations of High-Level Synthesis Codes for High-Performance Computing

Johannes de Fine Licht, Simon Meierhans, Torsten Hoefler

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Tags: Computer science, FPGA, HLS, OpenCL

May 26, 2018 by hgpu

Parallel Programming for FPGAs

Ryan Kastner, Janarbek Matai, Stephen Neuendorffer

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Tags: Book, Computer science, FPGA, HLS

May 12, 2018 by hgpu

Efficient Hardware Acceleration on SoC-FPGA with OpenCL

Susmitha Gogineni

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Tags: Computer science, Design space exploration, FPGA, Heterogeneous systems, HLS, OpenCL, Thesis

May 12, 2018 by hgpu

Fast inference of deep neural networks in FPGAs for particle physics

Javier Duarte, Song Han, Philip Harris, Sergo Jindariani, Edward Kreinar, Benjamin Kreis, Jennifer Ngadiuba, Maurizio Pierini, Nhan Tran, Zhenbin Wu

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Tags: Data acquisition, Deep learning, FPGA, HLS, Instrumentation and Detectors, Machine learning, Package, Physics

April 22, 2018 by hgpu

Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs

Chuanhao Zhuge, Xinheng Liu, Xiaofan Zhang, Sudeep Gummadi, Jinjun Xiong, Deming Chen

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Tags: CNN, Computer science, Computer vision, FFT, FPGA, HLS

March 31, 2018 by hgpu

Accelerating CNN inference on FPGAs: A Survey

Kamel Abdelouahab, Maxime Pelcat, Jocelyn Serot, Francois Berry

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Tags: Computer science, Computer vision, Deep learning, FPGA, HLS, Neural networks, OpenCL

March 25, 2018 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

Spatial: A Language and Compiler for Application Accelerators

A High-efficiency FPGA-based Accelerator for Convolutional Neural Networks using Winograd Algorithm

Transformations of High-Level Synthesis Codes for High-Performance Computing

Parallel Programming for FPGAs

Efficient Hardware Acceleration on SoC-FPGA with OpenCL

Fast inference of deep neural networks in FPGAs for particle physics

Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs

Accelerating CNN inference on FPGAs: A Survey

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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