Search Results for author:
Found 6 papers, 2 papers with code
Fast Support Vector Machines Using Parallel Adaptive Shrinking on Distributed Systems
Under sample elimination, several heuristics for {\em earliest possible} to {\em lazy} elimination of non-contributing samples are proposed.
Performance Analysis of Deep Learning Workloads on Leading-edge Systems
This work examines the performance of leading-edge systems designed for machine learning computing, including the NVIDIA DGX-2, Amazon Web Services (AWS) P3, IBM Power System Accelerated Compute Server AC922, and a consumer-grade Exxact TensorEX TS4 GPU server.
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
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
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
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.
