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WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

Tian Lan, Sunil Srinivasa, Stephan Zheng

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Tags: Computer science, CUDA, Machine learning, nVidia, Package, Python, Tesal V100

September 5, 2021 by hgpu

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

ProGraML: Program Graphs for Machine Learning

PROGRAML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations

Datoviz: GPU interactive scientific data visualization with Vulkan

High-Performance Interactive Scientific Visualization With Datoviz via the Vulkan Low-Level GPU API

NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

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