high performance computing on graphics processing units: hgpu.org

Jianhui Li, Zhennan Qin, Yijie Mei, Jingze Cui, Yunfei Song, Ciyong Chen, Yifei Zhang, Longsheng Du, Xianhang Cheng, Baihui Jin, Jason Ye, Eric Lin, Dan Lavery

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Tags: Code generation, Compilers, Computer science, Deep learning, OpenCL, Package

January 8, 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

Extending MAGMA Portability with OneAPI

Anna Fortenberry, Stanimire Tomov

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Tags: Computer science, CUDA, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce RTX 3060, oneAPI, Package, performance portability

December 25, 2022 by hgpu

GPU Load Balancing

Muhammad Osama

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, load balancing, Matrix multiplication, nVidia, nVidia A100, Package, Sparse, Thesis

December 25, 2022 by hgpu

Kernel-as-a-Service: A Serverless Interface to GPUs

Nathan Pemberton, Anton Zabreyko, Zhoujie Ding, Randy Katz, Joseph Gonzalez

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Tags: Cloud, Computer science, CUDA, Deep learning, Distributed computing, nVidia, Package, Tesla V100

December 25, 2022 by hgpu

A Framework to Generate High-Performance Time-stepped Agent-based Simulations on Heterogeneous Hardware

Jiajian Xiao

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Tags: Computer science, FPGA, Heterogeneous systems, nVidia GeForce GTX 1060, nVidia GeForce RTX 2080 Ti, OpenCL, Thesis

December 19, 2022 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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high performance computing on graphics processing units: hgpu.org

Programming

A Programming Model for GPU Load Balancing

Distributed Calculations with Algorithmic Skeletons for Heterogeneous Computing Environments

Improving the scalability of modern applications by parallel multi-core and many-core programming

Myths and Legends in High-Performance Computing

A Domain-Extensible Compiler with Controllable Automation of Optimisations

Arax: a runtime framework for decoupling applications from heterogeneous accelerators

oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation

BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

Extending MAGMA Portability with OneAPI

GPU Load Balancing

A Framework to Generate High-Performance Time-stepped Agent-based Simulations on Heterogeneous Hardware

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Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

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INT v.s. FP: A framework to compare low-bit integer and float-point formats

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

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