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Reverse-Mode AD of Reduce-by-Index and Scan in Futhark

Lotte Maria Bruun, Ulrik Stuhr Larsen, Nikolaj Hinnerskov, Cosmin Oancea

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Tags: Computer science, nVidia, nVidia A100, Programming Languages

October 15, 2023 by hgpu

Comparing Performance and Portability between CUDA and SYCL for Protein Database Search on NVIDIA, AMD, and Intel GPUs

Manuel Costanzo, Enzo Rucci, Carlos García Sánchez, Marcelo Naiouf, Manuel Prieto-Matías

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Tags: AMD Radeon RX 6700 XT, ATI, Bioinformatics, Biology, Computer science, CUDA, Intel, Intel UHD 630, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3070, nVidia GeForce RTX 3090, oneAPI, Programming Languages, Smith-Waterman algorithm, SYCL, Tesla V100

September 24, 2023 by hgpu

host device — Generic programming in Cuda

Thomas Mejstrik

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Tags: Computer science, CUDA, nVidia, Programming Languages

September 17, 2023 by hgpu

Descend: A Safe GPU Systems Programming Language

Bastian Köpcke, Sergei Gorlatch, Michel Steuwer

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Tags: Computer science, CUDA, nVidia, OpenCL, Package, Programming Languages, Tesla P100

May 14, 2023 by hgpu

BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

Erik Hellsten, Artur Souza, Johannes Lenfers, Rubens Lacouture, Olivia Hsu, Adel Ejjeh, Fredrik Kjolstad, Michel Steuwer, Kunle Olukotun, Luigi Nardi

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Tags: Bayesian, Compilers, Computer science, FPGA, nVidia, OpenCL, Package, Performance, Programming Languages, Tesla K80

January 8, 2023 by hgpu

High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs

William S. Moses, Ivan R. Ivanov, Jens Domke, Toshio Endo, Johannes Doerfert, Oleksandr Zinenko

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce RTX 2080 Ti, Package, performance portability, Programming Languages

July 10, 2022 by hgpu

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

CuPBoP-AMD: Extending CUDA to AMD Platforms

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

AFOCL: Portable OpenCL Programming of FPGAs via Automated Built-in Kernel Management

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

pp-template: Template for Performance-Portable code development, targeting HOST-DEVICE computer architectures

An approach to performance portability through generic programming

See all packages

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high performance computing on graphics processing units: hgpu.org

Reverse-Mode AD of Reduce-by-Index and Scan in Futhark

Comparing Performance and Portability between CUDA and SYCL for Protein Database Search on NVIDIA, AMD, and Intel GPUs

host device — Generic programming in Cuda

Descend: A Safe GPU Systems Programming Language

BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs

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

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Kernel Tuner

GT4Py: GridTools for Python

miniBUDE: A BUDE virtual-screening benchmark, in many programming models

gossip: Efficient Communication Primitives for Multi-GPU Systems

AUCMEDI: a framework for Automated Classification of Medical Images

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

AFOCL: Portable OpenCL Programming of FPGAs via Automated Built-in Kernel Management

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

pp-template: Template for Performance-Portable code development, targeting HOST-DEVICE computer architectures

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