high performance computing on graphics processing units: hgpu.org

Dong He, Supun Nakandala, Dalitso Banda, Rathijit Sen, Karla Saur, Kwanghyun Park, Carlo Curino, Jesús Camacho-Rodríguez, Konstantinos Karanasos, Matteo Interlandi

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Tags: Benchmarking, Cloud, Computer science, CUDA, Databases, Machine learning, nVidia, Package, Tesla P100

March 6, 2022 by hgpu

TCUDB: Accelerating Database with Tensor Processors

Yu-Ching Hu, Yuliang Li, Hung-Wei Tseng

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Tags: Computer science, CUDA, Databases, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce RTX 3090

December 19, 2021 by hgpu

Accelerating AutoDock VINA with GPUs

Shidi Tang, Ruiqi Chen, Mengru Lin, Qingde Lin, Yanxiang Zhu, Jiansheng Wu, Haifeng Hu, Ming Ling

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Tags: Chemistry, Databases, Heterogeneous systems, molecular docking, nVidia, nVidia GeForce GTX 1060, nVidia GeForce RTX 2080 Ti, nVidia GeForce RTX 3090, OpenCL, Package

October 17, 2021 by hgpu

The Art of Balance: A RateupDB Experience of Building a CPU/GPU Hybrid Database Product

Rubao Lee, Minghong Zhou, Chi Li, Shenggang Hu, Jianping Teng, Dongyang Li, Xiaodong Zhang

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, nVidia Quadro RTX 8000

August 29, 2021 by hgpu

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Descend: A Safe GPU Systems Programming Language

Redwood heterogeneous framework

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

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high performance computing on graphics processing units: hgpu.org

RTIndeX: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing

Revisiting Query Performance in GPU Database Systems

SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Novel Parallel Approaches to Efficiently Solve Spatial Problems on Heterogeneous CPU-GPU Systems

An experimental study of group-by and aggregation on CPU-GPU processors

End-to-end Optimization of Machine Learning Prediction Queries

PM4Py-GPU: a High-Performance General-Purpose Library for Process Mining

TCUDB: Accelerating Database with Tensor Processors

Accelerating AutoDock VINA with GPUs

The Art of Balance: A RateupDB Experience of Building a CPU/GPU Hybrid Database Product

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Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

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spotPython: Sequential Parameter Optimization in Python

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