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Found 4 papers, 3 papers with code
Learning Independent Program and Architecture Representations for Generalizable Performance Modeling
This paper proposes PerfVec, a novel deep learning-based performance modeling framework that learns high-dimensional, independent/orthogonal program and microarchitecture representations.
Dr. Top-k: Delegate-Centric Top-k on GPUs
Recent top-$k$ computation efforts explore the possibility of revising various sorting algorithms to answer top-$k$ queries on GPUs.
SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning
While discrete-event simulators are essential tools for architecture research, design, and development, their practicality is limited by an extremely long time-to-solution for realistic applications under investigation.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
C-SAW: A Framework for Graph Sampling and Random Walk on GPUs
In this paper, we propose, to the best of our knowledge, the first GPU-based framework for graph sampling/random walk.
Graph Sampling
Distributed, Parallel, and Cluster Computing
