high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

Mahdi Taheri, Mohamad Riazati, Mohammad Hasan Ahmadilivani, Maksim Jenihhin, Masoud Daneshtalab, Jaan Raik, Mikael Sjodin, Bjorn Lisper

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Tags: Computer science, Deep learning, Design space exploration, FPGA, HLS, Neural networks, Package

March 5, 2023 by hgpu

RTIndeX: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing

Justus Henneberg, Felix Schuhknecht

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Tags: Computer science, CUDA, Databases, nVidia, nVidia RTX A6000, Package, Raytracing

March 5, 2023 by hgpu

Interconnect Bandwidth Heterogeneity on AMD MI250x and Infinity Fabric

Carl Pearson

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Tags: AMD Radeon Instinct MI250X, ATI, Computer science, HIP, Performance

March 5, 2023 by hgpu

Porting numerical integration codes from CUDA to oneAPI: a case study

Ioannis Sakiotis, Kamesh Arumugam, Marc Paterno, Desh Ranjan, Balsa Terzic, Mohammad Zubair

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Tags: Computer science, CUDA, nVidia, nVidia V100, Package, SYCL

February 26, 2023 by hgpu

Auto-SpMV: Automated Optimizing SpMV Kernels on GPU

Mina Ashoury, Mohammad Loni, Farshad Khunjush, Masoud Daneshtalab

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Tags: Computer science, CUDA, Energy efficiency, Linear Algebra, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 1650, Sparse matrix

February 26, 2023 by hgpu

Orca: FSS-based Secure Training with GPUs

Neha Jawalkar, Kanav Gupta, Arkaprava Basu, Nishanth Chandran, Divya Gupta, Rahul Sharma

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Tags: Computer science, CUDA, nVidia, nVidia RTX A6000, Security

February 26, 2023 by hgpu

HLSDataset: Open-Source Dataset for ML-Assisted FPGA Design using High Level Synthesis

Zhigang Wei, Aman Arora, Lizy K. John

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Tags: Computer science, FPGA, Hardware Architecture, HLS, Machine learning, Package

February 26, 2023 by hgpu

GPU Offloading in ExaHyPE Through C++ Standard Algorithms

Uzmar Gomez, Gonzalo Brito Gadeschi, Tobias Weinzierl

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Tags: Algorithms, Computer science, CUDA, Differential equations, Mathematical Software, nVidia, nVidia GeForce RTX 2080 Ti, OpenMP, Partial differential equations, PDEs

February 26, 2023 by hgpu

A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)

Chellammal Surianarayanan, John Jeyasekaran Lawrence, Pethuru Raj Chelliah, Edmond Prakash, Chaminda Hewage

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Tags: Artificial intelligence, Computer science, Energy-efficient computing, Optimization, survey

February 12, 2023 by hgpu

EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs

Manuel de Castro, Inmaculada Santamaria-Valenzuela, Yuri Torres, Arturo Gonzalez-Escribano, Diego R. Llanos

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Tags: AMD FirePro W9100, ATI, Computer science, Heterogeneous systems, Numerical simulation, nVidia, OpenCL, Package, Stencil computation, Tesla V100

February 12, 2023 by hgpu

Improving Performance of Hardware Accelerators by Optimizing Data Movement: A Bioinformatics Case Study

Peter Knoben, Nikolaos Alachiotis

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Tags: Bioinformatics, Biology, Computer science, FPGA, OpenCL, Package

February 12, 2023 by hgpu

TLP: A Deep Learning-based Cost Model for Tensor Program Tuning

Yi Zhai, Yu Zhang, Shuo Liu, Xiaomeng Chu, Jie Peng, Jianmin Ji, Yanyong Zhang

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Tags: Computer science, CUDA, Deep learning, nVidia, Optimization, Package, Tesla K80, Tesla T4

February 12, 2023 by hgpu

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Clava: C/C++ Source-to-Source Tool based on Clang

Challenges and Opportunities in C/C++ Source-To-Source Compilation

gpuLik

Statistical Computing With Graphics Processing Units

Octo-Tiger: Astrophysics program simulating the evolution of star systems based on the fast multipole method on adaptive Octrees

Stellar Mergers with HPX-Kokkos and SYCL: Methods of using an Asynchronous Many-Task Runtime System with SYCL

BAT: A standardized benchmark suite for auto-tuners

Towards a Benchmarking Suite for Kernel Tuners

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE

EvoTorch: Scalable Evolutionary Computation in Python

DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

RTIndeX: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

RTIndeX: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing

Interconnect Bandwidth Heterogeneity on AMD MI250x and Infinity Fabric

Porting numerical integration codes from CUDA to oneAPI: a case study

Auto-SpMV: Automated Optimizing SpMV Kernels on GPU

Orca: FSS-based Secure Training with GPUs

HLSDataset: Open-Source Dataset for ML-Assisted FPGA Design using High Level Synthesis

GPU Offloading in ExaHyPE Through C++ Standard Algorithms

A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)

EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs

Improving Performance of Hardware Accelerators by Optimizing Data Movement: A Bioinformatics Case Study

TLP: A Deep Learning-based Cost Model for Tensor Program Tuning

Recent source codes

Clava: C/C++ Source-to-Source Tool based on Clang

gpuLik

Octo-Tiger: Astrophysics program simulating the evolution of star systems based on the fast multipole method on adaptive Octrees

BAT: A standardized benchmark suite for auto-tuners

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE

DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

RTIndeX: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing

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