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Found 10 papers, 4 papers with code
FT K-means: A High-Performance K-means on GPU with Fault Tolerance
K-means is a widely used algorithm in clustering, however, its efficiency is primarily constrained by the computational cost of distance computing.
FedFa: A Fully Asynchronous Training Paradigm for Federated Learning
We propose a full asynchronous training paradigm, called FedFa, which can guarantee model convergence and eliminate the waiting time completely for federated learning by using a few buffered results on the server for parameter updating.
Understanding The Effectiveness of Lossy Compression in Machine Learning Training Sets
We designed a systematic methodology for evaluating data reduction techniques for ML/AI, and we use it to perform a very comprehensive evaluation with 17 data reduction methods on 7 ML/AI applications to show modern lossy compression methods can achieve a 50-100x compression ratio improvement for a 1% or less loss in quality.
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks
The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data.
Exploring Autoencoder-based Error-bounded Compression for Scientific Data
(1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model.
cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data
To the best of our knowledge, cuSZ is the first error-bounded lossy compressor on GPUs for scientific data.
Distributed, Parallel, and Cluster Computing
FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks
(1) We propose several systematic ABFT schemes based on checksum techniques and analyze their fault protection ability and runtime thoroughly. Unlike traditional ABFT based on matrix-matrix multiplication, our schemes support any convolution implementations.
DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression
In this paper, we propose DeepSZ: an accuracy-loss bounded neural network compression framework, which involves four key steps: network pruning, error bound assessment, optimization for error bound configuration, and compressed model generation, featuring a high compression ratio and low encoding time.
Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization
One serious challenge is that the data prediction has to be performed based on the preceding decompressed values during the compression in order to guarantee the error bounds, which may degrade the prediction accuracy in turn.
Information Theory Information Theory
Z-checker: A Framework for Assessing Lossy Compression of Scientific Data
However, lossy compressor developers and users are missing a tool to explore the features of scientific datasets and understand the data alteration after compression in a systematic and reliable way.
Other Computer Science Instrumentation and Methods for Astrophysics Computational Engineering, Finance, and Science
