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Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers

J.D. Tyzack, H.Y. Mussa, M.J. Williamson,J. Kirchmair, R.C. Glen.

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Tags: Biochemistry, CUDA, Machine learning, nVidia, nVidia GeForce GT 640, nVidia Tesla C2075

May 31, 2014 by hgpu

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KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

Three Contributions to the Theory and Practice of Optimizing Compilers

HETU: a high-performance distributed deep learning system targeting large-scale and automated distributed training

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

SYCL-DNN: a library implementing neural network algorithms written using SYCL

User-Driven Online Kernel Fusion for SYCL

SW#SYCL

Assessing Opportunities of SYCL and Intel oneAPI for Biological Sequence Alignment

Flink-TornadoVM-Artifact

Challenges and Techniques for Transparent Acceleration of Unmodified Big Data Applications

StateMover: a checkpoint-based debugging framework for FPGAs

Hardware Checkpointing and Productive Debugging Flows for FPGAs

Kernel Tuner

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

TorchOpt: an efficient library for differentiable optimization built upon PyTorch

TorchOpt: An Efficient Library for Differentiable Optimization

NaturalCC: An Open-Source Toolkit for Code Intelligence

A Study on Neural-based Code Summarization in Low-resource Settings

mt4g: Memory Topology for GPUs

Capturing the Memory Topology of GPUs

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