Search Results - "Proceedings - IEEE International Parallel and Distributed Processing Symposium"

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…”
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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…”
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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…”
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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)…”
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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…”
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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…”
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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…”
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ByteTransformer: A High-Performance Transformer Boosted for Variable-Length Inputs by Zhai, Yujia, Jiang, Chengquan, Wang, Leyuan, Jia, Xiaoying, Zhang, Shang, Chen, Zizhong, Liu, Xin, Zhu, Yibo

“…Transformers have become keystone models in natural language processing over the past decade. They have achieved great popularity in deep learning…”
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HACCS: Heterogeneity-Aware Clustered Client Selection for Accelerated Federated Learning by Wolfrath, Joel, Sreekumar, Nikhil, Kumar, Dhruv, Wang, Yuanli, Chandra, Abhishek

“…Federated Learning is a machine learning paradigm where a global model is trained in-situ across a large number of distributed edge devices. While this…”
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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…”
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Accurate, Efficient and Scalable Graph Embedding by Zeng, Hanqing, Zhou, Hongkuan, Srivastava, Ajitesh, Kannan, Rajgopal, Prasanna, Viktor

“…The Graph Convolutional Network (GCN) model and its variants are powerful graph embedding tools for facilitating classification and clustering on graphs…”
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Cpp-Taskflow: Fast Task-Based Parallel Programming Using Modern C++ by Huang, Tsung-Wei, Lin, Chun-Xun, Guo, Guannan, Wong, Martin

“…In this paper we introduce Cpp-Taskflow, a new C++ tasking library to help developers quickly write parallel programs using task dependency graphs…”
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Lossy Scientific Data Compression With SPERR by Li, Shaomeng, Lindstrom, Peter, Clyne, John

“…As the need for data reduction in high-performance computing (HPC) continues to grow, we introduce a new and highly effective tool to help achieve this…”
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DistrEdge: Speeding up Convolutional Neural Network Inference on Distributed Edge Devices by Hou, Xueyu, Guan, Yongjie, Han, Tao, Zhang, Ning

“…As the number of edge devices with computing resources (e.g., embedded GPUs, mobile phones, and laptops) in-creases, recent studies demonstrate that it can be…”
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DLHub: Model and Data Serving for Science by Chard, Ryan, Li, Zhuozhao, Chard, Kyle, Ward, Logan, Babuji, Yadu, Woodard, Anna, Tuecke, Steven, Blaiszik, Ben, Franklin, Michael J., Foster, Ian

“…While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable…”
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VeloC: Towards High Performance Adaptive Asynchronous Checkpointing at Large Scale by Nicolae, Bogdan, Moody, Adam, Gonsiorowski, Elsa, Mohror, Kathryn, Cappello, Franck

“…Global checkpointing to external storage (e.g., a parallel file system) is a common I/O pattern of many HPC applications. However, given the limited I/O…”
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An Efficient 2D Method for Training Super-Large Deep Learning Models by Xu, Qifan, You, Yang

“…Since the rise of Transformer [22] and BERT [6], large language models [7], [12] have been proposed and shown unprecedented performance in tasks like…”
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FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks by Rahman, Md. Khaledur, Sujon, Majedul Haque, Azad, Ariful

“…We develop a fused matrix multiplication kernel that unifies sampled dense-dense matrix multiplication and sparsedense matrix multiplication under a single…”
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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…”
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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…”
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