2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)最新文献_第7页

DeepThermo: Deep Learning Accelerated Parallel Monte Carlo Sampling for Thermodynamics Evaluation of High Entropy Alloys

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2023-05-01 DOI: 10.1109/IPDPS54959.2023.00041

Junqi Yin, Feiyi Wang, M. Shankar

引用次数: 0

QoS-Aware and Cost-Efficient Dynamic Resource Allocation for Serverless ML Workflows 无服务器ML工作流的qos感知和成本效益动态资源分配

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2023-05-01 DOI: 10.1109/IPDPS54959.2023.00093

Hao Wu, Junxiao Deng, Haoqiang Fan, Shadi Ibrahim, Song Wu, Hai Jin

{"title":"QoS-Aware and Cost-Efficient Dynamic Resource Allocation for Serverless ML Workflows","authors":"Hao Wu, Junxiao Deng, Haoqiang Fan, Shadi Ibrahim, Song Wu, Hai Jin","doi":"10.1109/IPDPS54959.2023.00093","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00093","url":null,"abstract":"Machine Learning (ML) workflows are increasingly deployed on serverless computing platforms to benefit from their elasticity and fine-grain pricing. Proper resource allocation is crucial to achieve fast and cost-efficient execution of serverless ML workflows (specially for hyperparameter tuning and model training). Unfortunately, existing resource allocation methods are static, treat functions equally, and rely on offline prediction, which limit their efficiency. In this paper, we introduce CE-scaling – a Cost-Efficient autoscaling framework for serverless ML work-flows. During the hyperparameter tuning, CE-scaling partitions resources across stages according to their exact usage to minimize resource waste. Moreover, it incorporates an online prediction method to dynamically adjust resources during model training. We implement and evaluate CE-scaling on AWS Lambda using various ML models. Evaluation results show that compared to state-of-the-art static resource allocation methods, CE-scaling can reduce the job completion time and the monetary cost by up to 63% and 41% for hyperparameter tuning, respectively; and by up to 58% and 38% for model training.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114390239","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

Distributed Sparse Random Projection Trees for Constructing K-Nearest Neighbor Graphs 构造k近邻图的分布稀疏随机投影树

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2023-05-01 DOI: 10.1109/IPDPS54959.2023.00014

Isuru Ranawaka, Md. Khaledur Rahman, A. Azad

引用次数: 0

Mimir: Extending I/O Interfaces to Express User Intent for Complex Workloads in HPC Mimir:扩展I/O接口以表达HPC中复杂工作负载的用户意图

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2023-05-01 DOI: 10.1109/IPDPS54959.2023.00027

H. Devarajan, K. Mohror

{"title":"Mimir: Extending I/O Interfaces to Express User Intent for Complex Workloads in HPC","authors":"H. Devarajan, K. Mohror","doi":"10.1109/IPDPS54959.2023.00027","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00027","url":null,"abstract":"The complexity of data management in HPC systems stems from the diversity in I/O behavior exhibited by new workloads, multistage workflows, and the presence of multitiered storage systems. This complexity is managed by the storage systems, which provide user-level configurations to allow the tuning of workload I/O within the system. However, these configurations are difficult to set by users who lack expertise in I/O subsystems. We propose a paradigm change in which users specify the intent of I/O operations and storage systems automatically set various configurations based on the supplied intent. To this end, we developed the Mimir infrastructure to assist users in passing I/O intent to the underlying storage system. We demonstrate several use cases that map user-defined intents to storage configurations that lead to optimized I/O. In this study, we make three observations. First, I/O intents should be applied to each level of the I/O storage stack, from HDF5 to MPI-IO to POSIX, and integrated using lightweight adaptors in the existing stack. Second, the Mimir infrastructure supports up to 400M Ops/sec throughput of intents in the system, with a low memory overhead of 6.85KB per node. Third, intents assist in configuring a hierarchical cache to preload I/O, buffer in a node-local device, and store data in a global cache to optimize I/O workloads by 2.33×, 4×, and 2.1×, respectively. Our Mimir infrastructure optimizes complex large-scale workflows by up to 4× better I/O performance on the Lassen supercomputer by using automatically derived I/O intents.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125917028","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

H-Cache: Traffic-Aware Hybrid Rule-Caching in Software-Defined Networks H-Cache:软件定义网络中流量感知混合规则缓存

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2023-05-01 DOI: 10.1109/IPDPS54959.2023.00017

Zeyu Luan, Qing Li, Yi Wang, Yong Jiang

{"title":"H-Cache: Traffic-Aware Hybrid Rule-Caching in Software-Defined Networks","authors":"Zeyu Luan, Qing Li, Yi Wang, Yong Jiang","doi":"10.1109/IPDPS54959.2023.00017","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00017","url":null,"abstract":"Ternary Content Addressable Memory (TCAM) is an essential hardware component in SDN-enabled switches, which supports fast lookup speed and flexible matching patterns. However, TCAM’s limited storage capacity has long been a scalability challenge to enforce fine-grained forwarding policies in SDN. Based on the observation of traffic locality, the rule-caching mechanism employs a combination of TCAM and Random Access Memory (RAM) to maintain the forwarding rules of large and small flows, respectively. However, previous works cannot identify large flows timely and accurately, and suffer from high computational complexity when addressing rule dependencies in TCAM. Worse still, TCAM only caches the forwarding rules of large flows but ignores the latency requirements of small flows. Small flows encounter cache-miss in TCAM and then will be diverted to RAM, where they have to experience slow lookup processes. To jointly optimize the performance of both high-throughput large flows and latency-sensitive small flows, we propose a hybrid rule-caching framework, H-Cache, to scale traffic-aware forwarding policies in SDN. H-Cache identifies large flows through a collaboration of learning-based and threshold-based methods to achieve early detection and high accuracy, and proposes a time-efficient greedy heuristic to address rule dependencies. For small flows, H-Cache establishes default paths in TCAM to speed up their lookup processes, and also reduces their TCAM occupancy through label switching and region partitioning. Experiments with both real-world and synthetic datasets demonstrate that H-Cache increases TCAM utilization by an average of 11% and reduces the average completion time of small flows by almost 70%.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114692561","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

Designing and Optimizing GPU-aware Nonblocking MPI Neighborhood Collective Communication for PETSc* 面向PETSc的gpu感知非阻塞MPI邻域集体通信设计与优化

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2023-05-01 DOI: 10.1109/IPDPS54959.2023.00070

Kawthar Shafie Khorassani, Chen-Chun Chen, H. Subramoni, D. Panda

{"title":"Designing and Optimizing GPU-aware Nonblocking MPI Neighborhood Collective Communication for PETSc*","authors":"Kawthar Shafie Khorassani, Chen-Chun Chen, H. Subramoni, D. Panda","doi":"10.1109/IPDPS54959.2023.00070","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00070","url":null,"abstract":"MPI Neighborhood collectives are used for non-traditional collective operations involving uneven distribution of communication amongst processes such as sparse communication patterns. They provide flexibility to define the communication pattern involved when a neighborhood relationship can be defined. PETSc, the Portable, Extensible Toolkit for Scientific Computation, used extensively with scientific applications to provide scalable solutions through routines modeled by partial differential equations, utilizes neighborhood communication patterns to define various structures and routines.We propose GPU-aware MPI Neighborhood collective operations with support for AMD and NVIDIA GPU backends and propose optimized designs to provide scalable performance for various communication routines. We evaluate our designs using PETSc structures for scattering from a parallel vector to a parallel vector, scattering from a sequential vector to a parallel vector, and scattering from a parallel vector to a sequential vector using a star forest graph representation implemented with nonblocking MPI neighborhood alltoallv collective operations. We evaluate our neighborhood designs on 64 NVIDIA GPUs on the Lassen system with Infiniband networking, demonstrating30.90% improvement against a GPU implementation utilizing CPU-staging techniques, and 8.25% improvement against GPU-aware point-to-point implementations of the communication pattern. We also evaluate on 64 AMD GPUs on the Spock system with slingshot networking and present 39.52% improvement against the CPU-staging implementation of a neighborhood GPU vector type in PETSc, and 33.25% improvement against GPU-aware point-to-point implementation of the routine.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"252 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129981478","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 Machine Learning Approach Towards Runtime Optimisation of Matrix Multiplication 矩阵乘法运行时优化的机器学习方法

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2023-05-01 DOI: 10.1109/IPDPS54959.2023.00059

Yufan Xia, M. D. L. Pierre, A. Barnard, Giuseppe M. J. Barca

引用次数: 0

Lossy Scientific Data Compression With SPERR 有损科学数据压缩与SPERR

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2023-05-01 DOI: 10.1109/IPDPS54959.2023.00104

Shaomeng Li, P. Lindstrom, J. Clyne

引用次数: 5

Traversing Large Compressed Graphs on GPUs 在gpu上遍历大型压缩图

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2023-05-01 DOI: 10.1109/IPDPS54959.2023.00013

Prasun Gera, Hyesoon Kim

{"title":"Traversing Large Compressed Graphs on GPUs","authors":"Prasun Gera, Hyesoon Kim","doi":"10.1109/IPDPS54959.2023.00013","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00013","url":null,"abstract":"GPUs can be used effectively for accelerating graph analytics, provided the datasets fit in GPU memory. This is often not the case for large real-world datasets such as social, web, or biological graphs. We propose a graph compression format for static unweighted graphs based on Elias-Fano encoding that is amenable to run-time decompression on massively parallel architectures such as GPUs. We show that we can compress a variety of large graphs by a factor of 1.55x over the commonly used compressed sparse row (CSR) representation. The scheme is particularly beneficial for cases where conventional CSR based approaches do not work at all due to memory capacity constraints, or incur a significant penalty for out-of-core processing. We implement GPU accelerated breadth first search for this graph representation and show that the runtime performance for in-memory compressed graphs is 3.8x-6.5x better than out-of-core implementations for CSR graphs. Further, our implementation is also 1.45x-2x faster than the current state of the art in GPU based compressed graph traversals while maintaining a competitive compression ratio. We also extend our work to other analytics applications such as single source shortest paths and PageRank. Finally, we explore the interplay between graph reordering, graph compression, and performance.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130301512","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

Dynamic Tensor Linearization and Time Slicing for Efficient Factorization of Infinite Data Streams 无限数据流高效分解的动态张量线性化和时间切片

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2023-05-01 DOI: 10.1109/IPDPS54959.2023.00048

Yongseok Soh, Ahmed E. Helal, Fabio Checconi, Jan Laukemann, Jesmin Jahan Tithi, Teresa M. Ranadive, F. Petrini, Jeewhan Choi

{"title":"Dynamic Tensor Linearization and Time Slicing for Efficient Factorization of Infinite Data Streams","authors":"Yongseok Soh, Ahmed E. Helal, Fabio Checconi, Jan Laukemann, Jesmin Jahan Tithi, Teresa M. Ranadive, F. Petrini, Jeewhan Choi","doi":"10.1109/IPDPS54959.2023.00048","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00048","url":null,"abstract":"Streaming tensor factorization is an effective tool for unsupervised analysis of time-evolving sparse data, which emerge in many critical domains such as cybersecurity and trend analysis. In contrast to traditional tensors, time-evolving tensors demonstrate extreme sparsity and sparsity variation over time, resulting in irregular memory access and inefficient use of parallel computing resources. Additionally, due to the prohibitive cost of dynamically generating compressed sparse tensor formats, the state-of-the-art approaches process streaming tensors in a raw form that fails to capture data locality and suffers from high synchronization cost. To address these challenges, we propose a new dynamic tensor linearization framework that quickly encodes streaming multi-dimensional data on-the-fly in a compact representation, which has substantially lower memory usage and higher data reuse and parallelism than the original raw data. This is achieved by using a spatial sketching algorithm that keeps all incoming nonzero elements but remaps them into a tensor sketch with considerably reduced multi-dimensional image space. Moreover, we present a dynamic time slicing mechanism that uses variable-width time slices (instead of the traditional fixed-width) to balance the frequency of factor updates and the utilization of computing resources. We demonstrate the efficacy of our framework by accelerating two high-performance streaming tensor algorithms, namely, CP-stream and spCP-stream, and significantly improve their performance for a range of real-world streaming tensors. On a modern 56-core CPU, our framework achieves 10.3 − 11× and 6.4 − 7.2× geometric-mean speedup for the CP-stream and spCP-stream algorithms, respectively.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"14 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132934399","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