high performance computing on graphics processing units: hgpu.org

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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

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

Cramming: Training a Language Model on a Single GPU in One Day

Jonas Geiping, Tom Goldstein

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Tags: Computer science, Deep learning, NLP, nVidia, nVidia GeForce RTX 2080 Ti, nVidia RTX A4000, nVidia RTX A6000, Package

January 8, 2023 by hgpu

Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU

Hanqiu Chen, Yahya Alhinai, Yihan Jiang, Eunjee Na, Cong Hao

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia RTX A6000, Package

October 16, 2022 by hgpu

Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering

Wenzel Jakob, Sébastien Speierer, Nicolas Roussel, Delio Vicini

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Tags: 3D Graphics and Realism, Algorithms, Code generation, CUDA, nVidia, nVidia RTX A6000, Python, Rendering

February 6, 2022 by hgpu

GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration

Stefan Abi-Karam, Yuqi He, Rishov Sarkar, Lakshmi Sathidevi, Zihang Qiao, Cong Hao

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Tags: Computer science, FPGA, Machine learning, Neural networks, nVidia, nVidia RTX A6000, Package

January 30, 2022 by hgpu

Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

Martin Uray, Eduard Hirsch, Gerold Katzinger, Michael Gadermayr

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Tags: Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, nVidia RTX A6000

October 17, 2021 by hgpu

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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

See all packages

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high performance computing on graphics processing units: hgpu.org

RTIndeX: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing

Orca: FSS-based Secure Training with GPUs

Cramming: Training a Language Model on a Single GPU in One Day

Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU

Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering

GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration

Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

Recent source codes

E2C-Sim: a simulator and workload generator for Edge-to-Cloud environments

Kernel Tuner

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

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