2022 IEEE 29th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)最新文献

Hands-On, Instructor-Light, Checked and Tracked Training of Trainers in Java Fork-Join Abstractions 在Java Fork-Join抽象的培训师的实践，讲师轻，检查和跟踪培训

2022 IEEE 29th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) Pub Date : 2022-12-01 DOI: 10.1109/HiPCW57629.2022.00009

P. Dewan, A. Worley, Samuel George, Felipe Yanaga, Andrew Wortas, James Juschuk, Mike Rogers, Sheikh Ghafoor

{"title":"Hands-On, Instructor-Light, Checked and Tracked Training of Trainers in Java Fork-Join Abstractions","authors":"P. Dewan, A. Worley, Samuel George, Felipe Yanaga, Andrew Wortas, James Juschuk, Mike Rogers, Sheikh Ghafoor","doi":"10.1109/HiPCW57629.2022.00009","DOIUrl":"https://doi.org/10.1109/HiPCW57629.2022.00009","url":null,"abstract":"As part of a 3-day workshop on training faculty members in concurrency, we developed a module for hands-on training in Java Fork-Join abstractions that had several related novel pedagogical and technical components: (1) Source and runtime checks that (a) tested whether test-aware code created by the trainees met the expected requirements and (b) logged their results in the local file system and the IBM cloud. (2) Editable worked example code along with a guide on how to understand the underlying concepts behind the code and experiment with the code. (3) The ability to follow the guide (a) synchronously, with graduate student help, in a session devoted to this module, and (b) asynchronously, on one's own, before or after the synchronous session. (4) Assignments trainees could do after experimenting with the worked example. (5) Zoom recording of the entire synchronous session. Fourteen faculty members across the country attended the session and had varying amounts of knowledge of Java and automatic assessment. Data gathered from check logs and a Zoom recording, together with novel visualizations of them, provide information to evaluate our pedagogical model and differentiate the participants.","PeriodicalId":432185,"journal":{"name":"2022 IEEE 29th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126738390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Towards Data Gravity and Compliance Aware Distributed Deep Learning on Hybrid Clouds 面向混合云的数据重力和遵从意识分布式深度学习

2022 IEEE 29th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) Pub Date : 2022-12-01 DOI: 10.1109/HiPCW57629.2022.00012

Avinash Maurya, Jaiaid Mobin, M. M. Rafique

{"title":"Towards Data Gravity and Compliance Aware Distributed Deep Learning on Hybrid Clouds","authors":"Avinash Maurya, Jaiaid Mobin, M. M. Rafique","doi":"10.1109/HiPCW57629.2022.00012","DOIUrl":"https://doi.org/10.1109/HiPCW57629.2022.00012","url":null,"abstract":"To store large volumes of data concurrently from a diverse set of sources, data stores such as data silos, lakes, and warehouses, have been widely embraced by various organizations. Thanks to data fabric architectures, such scattered data (both structurally and geographically), can be accessed transparently at scale while adhering to various administrative regulations (e.g. governance, privacy, compliance, etc.). However, modern workload schedulers and distributed deep learning (DDL) runtimes are oblivious to the uneven data distribution across different storage services and compliance regulations, leading to sub-optimal resource utilization and training completion times. Al-though state-of-art workflow schedulers such as Apache Hadoop Yarn, Horovod, etc. exploit data locality, they require application developers to explicitly map data and resources available across various cloud services during job submission. These approaches are redundant and counterproductive for next-generation data fabric architectures that feature automated transparency and compliance abstractions for accessing disparate data sources with uneven data distribution. To this end, we propose an algorithm based on greedy programming that leverages the meta-data catalog of data fabric to efficiently determine training schedules based on data gravity, compliance, and resource availability. Our simulations based on synthetic data and resource distribution profiles demonstrate significant improvements in execution times and resource utilization compared to traditional DDL scheduling approaches in hybrid multi-cloud environments.","PeriodicalId":432185,"journal":{"name":"2022 IEEE 29th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117254636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Hands-on Approach for Scalable Parallel Applications Development: From Testbed to Petascale 可伸缩并行应用程序开发的实践方法:从测试平台到千兆级

2022 IEEE 29th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) Pub Date : 2022-12-01 DOI: 10.1109/HiPCW57629.2022.00010

D. Aggarwal, A. Bokshi, Deep Lad

引用次数: 0

A Visual Guide to MPI All-to-all MPI全方位视觉指南

2022 IEEE 29th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) Pub Date : 2022-12-01 DOI: 10.1109/HiPCW57629.2022.00008

Nick Netterville, Ke Fan, Sidharth Kumar, Thomas Gilray

引用次数: 3

Verifiable and Practical Compliance for Data Privacy Laws 数据隐私法的可验证和实际遵守

2022 IEEE 29th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) Pub Date : 2022-12-01 DOI: 10.1109/HiPCW57629.2022.00013

M. Awasthi

{"title":"Verifiable and Practical Compliance for Data Privacy Laws","authors":"M. Awasthi","doi":"10.1109/HiPCW57629.2022.00013","DOIUrl":"https://doi.org/10.1109/HiPCW57629.2022.00013","url":null,"abstract":"A number of governments have legislated privacy laws in recent years. The most prominent international one covering multiple nations is the General Data Protection Regulation (GDPR) of the European Union. Many national and local governments are in the process of tabling similar legislation. To be compliant with privacy laws, software companies providing Software as a Service (SaaS) have changed internal practices to develop applications with a “privacy first” ethos. In addition, these companies (data controllers) have put mechanisms in place for ensuring the privacy and security preparedness of their service providers (data processors), which is currently being done manually using questionnaires. Questionnaires designed to collect compliance information from processors aren't the best instruments. This is due to many reasons including lack of clarity on information to be collected, humans in the information collection loop, and badly designed questionnaires, among others. In this paper, we analyse a few reasons making compliance determination a herculean tasks for both parties and propose a simple mechanism to automate compliance information gathering.","PeriodicalId":432185,"journal":{"name":"2022 IEEE 29th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129627507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Security Analysis of Labeling-Based Control-Flow Integrity Schemes 基于标签的控制流完整性方案的安全性分析

2022 IEEE 29th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) Pub Date : 2022-12-01 DOI: 10.1109/HiPCW57629.2022.00011

David Demicco, Matthew Cole, Shengdun Wang, Aravind Prakash

{"title":"A Security Analysis of Labeling-Based Control-Flow Integrity Schemes","authors":"David Demicco, Matthew Cole, Shengdun Wang, Aravind Prakash","doi":"10.1109/HiPCW57629.2022.00011","DOIUrl":"https://doi.org/10.1109/HiPCW57629.2022.00011","url":null,"abstract":"Secure and transparent policy enforcement by a cloud provider is crucial in cloud infrastructures. Particularly, enforcement of control-flow integrity (CFI) policy has been widely accepted for stopping software-induced attacks. Using low-level hardware metadata to encode CFI policy is a fairly recent development. Besides moving enforcement out of the software and into the hardware for performance benefit, tagging metadata also offers other benefits in the precision of defenses. We evaluate several different metadata layouts for CFI policy enforcement, and examine the layouts' effects on the number of valid forward edges remaining in a RISC-V binary after policy enforcement. Additionally we look at related work in tag-based tools that provide CFI policy enforcement in order to get a sense of their performance and the design trade-offs they make. We evaluate our policy and the related works in terms of space and precision trade-offs for forward- and backward-edge CFI, finding that some trade-offs have a higher impact on the number of remaining forward edges, notably return address protection. Additionally, we report that existing backward edge protections can be highly effective, reducing the number of remaining backward edges in a protected binary to an average of 0.034% over an equivalent coarse-grained CFI.","PeriodicalId":432185,"journal":{"name":"2022 IEEE 29th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123454455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Compendium of Abstracts 摘要汇编

2022 IEEE 29th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) Pub Date : 2022-12-01 DOI: 10.1109/HiPCW57629.2022.00015

M. Suresh, D. Banerjee

引用次数: 0