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Reusing Auto-Schedules for Efficient DNN Compilation

Perry Gibson, José Cano

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Tags: Algorithms, Computer science, Machine learning, Neural networks, Performance, Programming Languages

January 23, 2022 by hgpu

Unified Shader Programming in C++

Kerry A. Seitz Jr., Theresa Foley, Serban D. Porumbescu, John D. Owens

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Tags: Compilers, Computer science, HLSL, nVidia, nVidia Titan RTX, Programming Languages, Programming techniques, Real-time graphics

October 3, 2021 by hgpu

Supporting CUDA for an extended RISC-V GPU architecture

Ruobing Han, Blaise Tine, Jaewon Lee, Jaewoong Sim, Hyesoon Kim

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Tags: Computer science, CUDA, LLVM, nVidia, OpenCL, Package, Programming Languages

September 5, 2021 by hgpu

A Compiler Infrastructure for Accelerator Generators

Rachit Nigam, Samuel Thomas, Zhijing Li, Adrian Sampson

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Tags: Compilers, Computer science, DSL, Hardware Architecture, HLS, Package, Programming Languages

March 14, 2021 by hgpu

LazyTensor: combining eager execution with domain-specific compilers

Alex Suhan, Davide Libenzi, Ailing Zhang, Parker Schuh, Brennan Saeta, Jie Young Sohn, Denys Shabalin

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Tags: Computer science, Deep learning, Package, Programming Languages, TPU

March 7, 2021 by hgpu

C-for-Metal: High Performance SIMD Programming on Intel GPUs

Guei-Yuan Lueh, Kaiyu Chen, Gang Chen, Joel Fuentes, Wei-Yu Chen, Fangwen Fu, Hong Jiang, Hongzheng Li, Daniel Rhee

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Tags: Computer science, CUDA, nVidia, OpenCL, Package, Programming Languages

January 31, 2021 by hgpu

NaturalCC: A Toolkit to Naturalize the Source Code Corpus

Yao Wan, Yang He, Jian-Guo Zhang, Yulei Sui, Hai Jin, Guandong Xu, Caiming Xiong, Philip S. Yu

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, Package, Programming Languages, Python, Video

December 13, 2020 by hgpu

Transparent Compiler and Runtime Specializations for Accelerating Managed Languages on FPGAs

Michail Papadimitriou, Juan Fumero, Athanasios Stratikopoulos, Foivos S. Zakkak, Christos Kotselidis

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Tags: Computer science, FPGA, HLS, Java, OpenCL, Optimization, Package, Programming Languages

November 8, 2020 by hgpu

AnyHLS: High-Level Synthesis with Partial Evaluation

M. Akif Özkan, Arsène Pérard-Gayot, Richard Membarth, Philipp Slusallek, Roland Leissa, Sebastian Hack, Jürgen Teich, Frank Hannig

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Tags: Computer science, FPGA, HLS, Image processing, OpenCL, Package, Programming Languages

August 9, 2020 by hgpu

Automatic Kernel Generation for Volta Tensor Cores

Somashekaracharya G. Bhaskaracharya, Julien Demouth, Vinod Grover

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Tags: Compilers, Computer science, CUBLAS, CUDA, Deep learning, Matrix multiplication, nVidia, nVidia Quadro GV100, Performance, Programming Languages, PTX

June 28, 2020 by hgpu

Parallel Programming Models for Heterogeneous Many-Cores: A Survey

Jianbin Fang, Chun Huang, Tao Tang, Zheng Wang

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Optimization, Programming Languages, Review

May 17, 2020 by hgpu

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather

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Tags: Computer science, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 970, OpenCL, Package, Performance, Programming Languages

March 29, 2020 by hgpu

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Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

On the Compilation Performance of Current SYCL Implementations

M1 GPUs for (scientific) computations in C++

Seamless GPU acceleration for C++ based physics with the Metal Shading Language on Apple’s M series unified chips

CUB: Cooperative primitives for CUDA C++

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Simulator and workload generator for Edge-to-Cloud environments

FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems

See all packages

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high performance computing on graphics processing units: hgpu.org

Reusing Auto-Schedules for Efficient DNN Compilation

Unified Shader Programming in C++

Supporting CUDA for an extended RISC-V GPU architecture

A Compiler Infrastructure for Accelerator Generators

LazyTensor: combining eager execution with domain-specific compilers

C-for-Metal: High Performance SIMD Programming on Intel GPUs

NaturalCC: A Toolkit to Naturalize the Source Code Corpus

Transparent Compiler and Runtime Specializations for Accelerating Managed Languages on FPGAs

AnyHLS: High-Level Synthesis with Partial Evaluation

Automatic Kernel Generation for Volta Tensor Cores

Parallel Programming Models for Heterogeneous Many-Cores: A Survey

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Recent source codes

Rodinia Benchmarks for DPC++

DCA++: a state of the art implementation of the dynamical cluster approximation

tntorch - Tensor Network Learning with PyTorch

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

M1 GPUs for (scientific) computations in C++

CUB: Cooperative primitives for CUDA C++

Simulator and workload generator for Edge-to-Cloud environments

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