high performance computing on graphics processing units: hgpu.org

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Kernel Launcher: C++ Library for Optimal-Performance Portable CUDA Applications

Stijn Heldens, Ben van Werkhoven

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Tags: Computer science, CUDA, Fluid dynamics, nVidia, nVidia A100, Package, Performance, performance portability

March 26, 2023 by hgpu

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

João Bispo, Nuno Paulino, Luís Miguel Sousa

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Tags: Compilers, Computer science, CUDA, nVidia, OpenCL, Package

March 19, 2023 by hgpu

Statistical Computing With Graphics Processing Units

Ruoyong Xu

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Tags: Computer science, Package, Pseudo-random number generators, R, Statistics, Thesis

March 19, 2023 by hgpu

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

Gregor Daiß, Patrick Diehl, Hartmut Kaiser, Dirk Pflüger

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Tags: AMD Radeon Instinct MI100, Astrophysics, Computer science, CUDA, HIP, nVidia, nVidia A100, Package, SYCL

March 19, 2023 by hgpu

Towards a Benchmarking Suite for Kernel Tuners

Jacob O. Tørring, Ben van Werkhoven, Filip Petrovic, Floris-Jan Willemsen, Jirí Filipovic, Anne C. Elster

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, nVidia GeForce RTX 3060, nVidia GeForce RTX 3090, nVidia Titan RTX, Package, performance portability

March 19, 2023 by hgpu

BenchDirect: A Directed Language Model for Compiler Benchmarks

Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim Hazelwood, Ajitha Rajan, Hugh Leather

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Tags: Benchmarking, Compilers, Computer science, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

March 12, 2023 by hgpu

A Deep Learning Model for Loop Interchange

Lina Mezdour, Khadidja Kadem, Massinissa Merouani, Amina Selma Haichour, Saman Amarasinghe, Riyadh Baghdadi

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Tags: Compilers, Computer science, Deep learning, nVidia, nVidia DGX-A100, Package

March 12, 2023 by hgpu

Bridging Control-Centric and Data-Centric Optimization

Tal Ben-Nun, Berke Ates, Alexandru Calotoiu, Torsten Hoefler

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Tags: Code generation, Compilers, Computer science, Package

March 12, 2023 by hgpu

EvoTorch: Scalable Evolutionary Computation in Python

Nihat Engin Toklu, Timothy Atkinson, Vojtěch Micka, Paweł Liskowski, Rupesh Kumar Srivastava

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Tags: Artificial intelligence, Computer science, CUDA, Evolutionary Computations, Neural and Evolutionary Computing, nVidia, Package, Python, PyTorch, Tesla V100

March 5, 2023 by hgpu

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

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

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

Packages

Kernel Launcher: C++ Library for Optimal-Performance Portable CUDA Applications

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

Statistical Computing With Graphics Processing Units

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

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

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

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

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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