Search Results - "Parallel and Distributed Processing"

Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems by Vasimuddin, Md, Misra, Sanchit, Li, Heng, Aluru, Srinivas

“…Innovations in Next-Generation Sequencing are enabling generation of DNA sequence data at ever faster rates and at very low cost. For example, the Illumina…”
Get full text

Comparative Performance Analysis of Intel (R) Xeon Phi (TM), GPU, and CPU: A Case Study from Microscopy Image Analysis by Teodoro, George, Kurc, Tahsin, Jun Kong, Cooper, Lee, Saltz, Joel

“…We study and characterize the performance of operations in an important class of applications on GPUs and Many Integrated Core (MIC) architectures. Our work is…”
Get full text

MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets by Hardy, Corentin, Le Merrer, Erwan, Sericola, Bruno

“…A recent technical breakthrough in the domain of machine learning is the discovery and the multiple applications of Generative Adversarial Networks (GANs)…”
Get full text

G-PBFT: A Location-based and Scalable Consensus Protocol for IoT-Blockchain Applications by Lao, Laphou, Dai, Xiaohai, Xiao, Bin, Guo, Songtao

“…IoT-blockchain applications have advantages of managing massive IoT devices, achieving advanced data security, and data credibility. However, there are still…”
Get full text

Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization by Dingwen Tao, Sheng Di, Zizhong Chen, Cappello, Franck

“…Today's HPC applications are producing extremely large amounts of data, such that data storage and analysis are becoming more challenging for scientific…”
Get full text

Smartly Handling Renewable Energy Instability in Supporting A Cloud Datacenter by Gao, Jiechao, Wang, Haoyu, Shen, Haiying

“…The size and energy consumption of datacenters have been increasing significantly over the past years. As a result, datacenters' increasing electricity…”
Get full text

Benchmarking Streaming Computation Engines: Storm, Flink and Spark Streaming by Chintapalli, Sanket, Dagit, Derek, Evans, Bobby, Farivar, Reza, Graves, Thomas, Holderbaugh, Mark, Zhuo Liu, Nusbaum, Kyle, Patil, Kishorkumar, Peng, Boyang Jerry, Poulosky, Paul

“…Streaming data processing has been gaining attention due to its application into a wide range of scenarios. To serve the booming demands of streaming data…”
Get full text

FedGroup: Efficient Federated Learning via Decomposed Similarity-Based Clustering by Duan, Moming, Liu, Duo, Ji, Xinyuan, Liu, Renping, Liang, Liang, Chen, Xianzhang, Tan, Yujuan

“…Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training…”
Get full text

Experience-Driven Computational Resource Allocation of Federated Learning by Deep Reinforcement Learning by Zhan, Yufeng, Li, Peng, Guo, Song

“…Federated learning is promising in enabling large-scale machine learning by massive mobile devices without exposing the raw data of users with strong privacy…”
Get full text

Weighted Feature Pyramid Networks for Object Detection by Li, Xiaohan, Lai, Taotao, Wang, Shuaiyu, Chen, Quan, Yang, Changcai, Chen, Riqing, Lin, Jinxun, Zheng, Fu

“…Feature Pyramid Networks (FPN) is a popular feature extraction. However, FPN and its variants do not investigate the influence of resolution information and…”
Get full text

NVIDIA Tensor Core Programmability, Performance & Precision by Markidis, Stefano, Chien, Steven Wei Der, Laure, Erwin, Peng, Ivy Bo, Vetter, Jeffrey S.

“…The NVIDIA Volta GPU microarchitecture introduces a specialized unit, called Tensor Core that performs one matrix-multiply-and-accumulate on 4x4 matrices per…”
Get full text

Why Globally Re-shuffle? Revisiting Data Shuffling in Large Scale Deep Learning by Nguyen, Truong Thao, Trahay, Francois, Domke, Jens, Drozd, Aleksandr, Vatai, Emil, Liao, Jianwei, Wahib, Mohamed, Gerofi, Balazs

“…Stochastic gradient descent (SGD) is the most prevalent algorithm for training Deep Neural Networks (DNN). SGD iterates the input data set in each training…”
Get full text

SPLATT: Efficient and Parallel Sparse Tensor-Matrix Multiplication by Smith, Shaden, Ravindran, Niranjay, Sidiropoulos, Nicholas D., Karypis, George

“…Multi-dimensional arrays, or tensors, are increasingly found in fields such as signal processing and recommender systems. Real-world tensors can be enormous in…”
Get full text

Fast Error-Bounded Lossy HPC Data Compression with SZ by Sheng Di, Cappello, Franck

“…Today's HPC applications are producing extremely large amounts of data, thus it is necessary to use an efficient compression before storing them to parallel…”
Get full text

Human Activity Recognition Using Federated Learning by Sozinov, Konstantin, Vlassov, Vladimir, Girdzijauskas, Sarunas

“…State-of-the-art deep learning models for human activity recognition use large amount of sensor data to achieve high accuracy. However, training of such models…”
Get full text

Distributed Louvain Algorithm for Graph Community Detection by Ghosh, Sayan, Halappanavar, Mahantesh, Tumeo, Antonino, Kalyanaraman, Ananth, Lu, Hao, Chavarria-Miranda, Daniel, Khan, Arif, Gebremedhin, Assefaw

“…In most real-world networks, the nodes/vertices tend to be organized into tightly-knit modules known as communities or clusters, such that nodes within a…”
Get full text

Communication-Efficient Jaccard similarity for High-Performance Distributed Genome Comparisons by Besta, Maciej, Kanakagiri, Raghavendra, Mustafa, Harun, Karasikov, Mikhail, Ratsch, Gunnar, Hoefler, Torsten, Solomonik, Edgar

“…The Jaccard similarity index is an important measure of the overlap of two sets, widely used in machine learning, computational genomics, information…”
Get full text

An Energy Efficiency Feature Survey of the Intel Haswell Processor by Hackenberg, Daniel, Schone, Robert, Ilsche, Thomas, Molka, Daniel, Schuchart, Joseph, Geyer, Robin

“…The recently introduced Intel Xeon E5-1600 v3 and E5-2600 v3 series processors -- codenamed Haswell-EP -- implement major changes compared to their…”
Get full text

Predicting the Price of Bitcoin Using Machine Learning by McNally, Sean, Roche, Jason, Caton, Simon

“…The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD can be predicted. The price data is sourced from the Bitcoin…”
Get full text

Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis by Collier, Nicholson, Wozniak, Justin M., Stevens, Abby, Babuji, Yadu, Binois, Mickael, Fadikar, Arindam, Wurth, Alexandra, Chard, Kyle, Ozik, Jonathan

“…COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new…”
Get full text