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…”
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

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

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

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

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…”
Get full text

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…”
Get full text

Disruptive Research and Innovation by Kai Li

“…Ever since Clayton Christensen coined the terms "disruptive technologies" and "disruptive innovations" in 1990s, researchers and entrepreneurs love the word…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
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

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…”
Get full text

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…”
Get full text

Graph Neural Network-Based Latency Prediction for Stream Processing Task by Chu, Zheng, Zhang, Ren Hang, Li, Baozhu, Ying, Changtian, Li, Weiyun

“…Latency prediction for stream processing tasks (SPTs) is a critical challenge in stream computing due to the real-time, mutable, unordered, infinite, and…”
Get full text

Achieving Better Benefits via Flexible Feature Matching in Post-Deduplication Delta Compression by Yang, Fengkui, Mao, Bo, Liu, Yuhan, Bao, Liang, Jiang, Weipeng, Zhang, Dongying, Li, Chunhua, Zhou, Ke

“…Cloud or distributed storage systems characterized by high data redundancy necessitate effective data reduction techniques to reduce storage costs…”
Get full text

MeanCache: User-Centric Semantic Caching for LLM Web Services by Gill, Waris, Elidrisi, Mohamed, Kalapatapu, Pallavi, Ahmed, Ammar, Anwar, Ali, Gulzar, Muhammad Ali

“…Large Language Models (LLMs) like ChatGPT and Llama have revolutionized natural language processing and search engine dynamics. However, these models incur…”
Get full text

Graph Input-Aware Matrix Multiplication for Pruned Graph Neural Network Acceleration by Khan, Hanan, Gurevin, Deniz, Khan, Omer

“…Graphs-based neural networks are powerful tools for analyzing intricate non-euclidean data structures to solve complex real-world problems bounded by latency…”
Get full text