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Susumu Date

Publications and source records attributed to Susumu Date.

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Performance Evaluation of RED-ONION: A High-Speed Disk-to-Disk Transfer System

Modern experimental instruments produce data faster than general-purpose file transfer interfaces can move it, so delivery to the computing infrastructure has become a bottleneck in the research process. At many universities and research institutes, moreover, the instruments that generate research data and the high-performance computing systems that analyze it are separated both geographically and organizationally, because each demands its own expertise and installation environment. Connecting the two seamlessly is a pressing challenge for data-driven science. This article presents RED-ONION, a high-speed disk-to-disk transfer system that connects research facilities, on campus and beyond, to a computing center. The system combines data transfer nodes, a dedicated high-bandwidth network, an all-flash parallel file system, and multi-threaded transfer software that parallelizes network transmission and storage access. The design targets the wire rate both along the entire path, from the read on the sender storage to the write on the receiver storage, and for a single file between one pair of nodes rather than only in aggregate over many files or nodes. We describe the end-to-end optimizations across the transfer software, the operating system, and the storage that this requires. We evaluate a prototype deployed over a 100 Gbps transpacific path between Atlanta and Tokyo with a 150 ms round-trip time, on which a single 1 TB file transfer reached 90 Gbps, delivering a terabyte in approximately 95 s. Moving a dataset of this size therefore becomes a routine step, and the computing center serves an instrument as if the two were co-located.

cs.DC

Performance analysis of mdx II: A next-generation cloud platform for cross-disciplinary data science research

mdx II is an Infrastructure-as-a-Service (IaaS) cloud platform designed to accelerate data science research and foster cross-disciplinary collaborations among universities and research institutions in Japan. Unlike traditional high-performance computing systems, mdx II leverages OpenStack to provide customizable and isolated computing environments consisting of virtual machines, virtual networks, and advanced storage. This paper presents a comprehensive performance evaluation of mdx II, including a comparison to Amazon Web Services (AWS). We evaluated the performance of a 16-vCPU VM from multiple aspects including floating-point computing performance, memory throughput, network throughput, file system and object storage performance, and real-world application performance. Compared to an AWS 16-vCPU instance, the results indicated that mdx II outperforms AWS in many aspects and demonstrated that mdx II holds significant promise for high-performance data analytics (HPDA) workloads. We also evaluated the virtualization overhead using a 224-vCPU VM occupying an entire host. The results suggested that the virtualization overhead is minimal for compute-intensive benchmarks, while memory-intensive benchmarks experienced larger overheads. These findings are expected to help users of mdx II to obtain high performance for their data science workloads and offer insights to the designers of future data-centric cloud platforms.

cs.DC

mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations

The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks. We have built such a nation-wide cloud platform, called "mdx" to meet this need. The mdx platform's virtualization service, jointly operated by 9 national universities and 2 national research institutes in Japan, launched in 2021, and more features are in development. Currently mdx is used by researchers in a wide variety of domains, including materials informatics, geo-spatial information science, life science, astronomical science, economics, social science, and computer science. This paper provides an the overview of the mdx platform, details the motivation for its development, reports its current status, and outlines its future plans.

cs.LG

PRAGMA-ENT: Exposing SDN Concepts to Domain Scientists in the Pacific Rim

The Pacific Rim Application and Grid Middleware Assembly (PRAGMA) is an international community of researchers that actively collaborate to address problems and challenges of common interest in eScience. The PRAGMA Experimental Network Testbed (PRAGMA-ENT) was established with the goal of constructing an international software-defined network (SDN) testbed to offer the necessary networking support to the PRAGMA cyberinfrastructure. PRAGMA-ENT is isolated, and PRAGMA researchers have complete freedom to access network resources to develop, experiment, and evaluate new ideas without the concerns of interfering with production networks. In the first phase, PRAGMA-ENT focused on establishing an international L2 backbone. With support from the Florida Lambda Rail (FLR), Internet2, PacificWave, JGN-X, and TWAREN, PRAGMA-ENT backbone connects Open\-Flow-enabled switches at University of Florida (UF), University of California San Diego (UCSD), Nara Institute of Science and Technology (NAIST, Japan), Osaka University (Japan), National Institute of Advanced Industrial Science and Technology (AIST, Japan), and National Center for High-Performance Computing (Taiwan). The second phase of PRAGMA-ENT consisted of evaluation of technologies for the control plane that enables multiple experiments (i.e., OpenFlow controllers) to co-exist. Preliminary experiments with FlowVisor revealed some limitations leading to the development of a new approach, called AutoVFlow. This paper will share our experience in the establishment of PRAGMA-ENT backbone (with international L2 links), its current status, and control plane plans. Discussion on preliminary application ideas, including optimization of routing control; multipath routing control; and remote visualization will also be discussed.

cs.NI