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BAT: A Benchmark suite for AutoTuners

Ingunn Sund, Knut A. Kirkhorn, Jacob O. Tørring, Anne C. Elster

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 980, Package, Performance, Tesla T4, Tesla V100

November 21, 2021 by hgpu

Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance

Jiarong Xing, Leyuan Wang, Shang Zhang, Jack Chen, Ang Chen, Yibo Zhu

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, Neural networks, nVidia, Performance, Tesla T4

October 31, 2021 by hgpu

LS-CAT: A Large-Scale CUDA AutoTuning Dataset

Lars Bjertnes, Jacob O. Tørring, Anne C. Elster

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Tags: Benchmarking, Computer science, CUDA, Databases, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 980, Tesla T4

April 5, 2021 by hgpu

DISC: A Dynamic Shape Compiler for Machine Learning Workloads

Kai Zhu, Wenyi Zhao, Zhen Zheng, Tianyou Guo, Pengzhan Zhao, Junjie Bai, Jun Yang, Xiaoyong Liu, Lansong Diao, Wei Lin

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Tags: Compilers, Comuter science, CUDA, Machine learning, nVidia, Tesla T4

March 14, 2021 by hgpu

Performance of CPU and GPU HPC Architectures for off-design aircraft simulation

Mike Turner, Jamil Appa, Neil Ashton

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Tags: cfd, CUDA, Fluid dynamics, MPI, nVidia, OpenMP, SYCL, Tesla A100, Tesla T4, Tesla V100

January 31, 2021 by hgpu

TopicBERT for Energy Efficient Document Classification

Yatin Chaudhary, Pankaj Gupta, Khushbu Saxena, Vivek Kulkarni, Thomas Runkler, Hinrich Schütze

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Tags: classification, Computer science, CUDA, Deep learning, NLP, nVidia, Tesla T4

November 8, 2020 by hgpu

A Comparison of Optimal Scanline Voxelization Algorithms

Tim Håkansson

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Tags: 3D Graphics and Realism, CUDA, GLSL, nVidia, OpenGL, Package, Tesla T4, Thesis, Voxelization

September 20, 2020 by hgpu

Optimizing Block-Sparse Matrix Multiplications on CUDA with TVM

Zijing Gu

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, nVidia, Package, Sparse matrix, Tesla T4

August 2, 2020 by hgpu

GPU coprocessors as a service for deep learning inference in high energy physics

Jeffrey Krupa, Kelvin Lin, Maria Acosta Flechas, Jack Dinsmore, Javier Duarte, Philip Harris, Scott Hauck, Burt Holzman, Shih-Chieh Hsu, Thomas Klijnsma, Mia Liu, Kevin Pedro, Natchanon Suaysom, Matt Trahms, Nhan Tran

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Tags: Deep learning, FPGA, HEP, nVidia, Package, Physics, Tesla T4, Tesla V100

July 26, 2020 by hgpu

clRNG: an OpenCL based software library containing random number generation functions

A tool set for random number generation on GPUs in R

DLRM: An implementation of a deep learning recommendation model

Building a Performance Model for Deep Learning Recommendation Model Training on GPUs

OpenMM: A High Performance Molecular Dynamics Library

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Fancier High-Performance API

Fancier: A Unified Framework for Java, C, and OpenCL Integration

A Compiler Framework for Optimizing Dynamic Parallelism on GPUs

A Compiler Framework for Optimizing Dynamic Parallelism on GPUs

GPOEO: a micro-intrusive GPU online energy optimization framework for iterative applications

Dynamic GPU Energy Optimization for Machine Learning Training Workloads

CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs

From Loop Fusion to Kernel Fusion: A Domain-specific Approach to Locality Optimization

System-Level Optimization and Code Generation for Graphics Processors using a Domain-Specific Language

Faster-Mean-Shift-Euc

GPU-accelerated Faster Mean Shift with euclidean distance metrics

Turbo: A constraint solver purely on GPUs (CUDA)

A Variant of Concurrent Constraint Programming on GPU

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