On Timely Staging of HPC Job Input Data
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Monti, Henri↗
Engineering topics
Monti, Henri
Publications and source records attributed to Monti, Henri.
Timely Result-Data Offloading for Improved HPC Center Scratch Provisioning and Serviceability
Modern High-Performance Computing (HPC) centers are facing a data deluge from emerging scientific applications. Supporting large data entails a significant commitment of the highthroughput center storage system, scratch space. However, the scratch space is typically managed using simple purge policies, without sophisticated end-user data services to balance resource consumption and user serviceability. End-user data services such as offloading are performed using point-to-point transfers that are unable to reconcile center s purge and users delivery deadlines, unable to adapt to changing dynamics in the end-toend data path and are not fault-tolerant. Such inefficiencies can be prohibitive to sustaining high performance. In this paper, we address the above issues by designing a framework for the timely, decentralized offload of application result data. Our framework uses an overlay of user-specified intermediate and landmark sites to orchestrate a decentralized fault-tolerant delivery. We have implemented our techniques within a production job scheduler (PBS) and data transfer tool (BitTorrent). Our evaluation using both a real implementation and supercomputer job log-driven simulations show that: the offloading times can be significantly reduced (90.4% for a 5 GB data transfer); the exposure window can be minimized while also meeting center-user Service Level Agreements.
99 GENERAL AND MISCELLANEOUS↗
CATCH: A Cloud-based Adaptive Data Transfer Service for HPC
Modern High Performance Computing (HPC) applications process very large amounts of data. A critical research challenge lies in transporting input data to the HPC center from a number of distributed sources, e.g., scientific experiments and web repositories, etc., and offloading the result data to geographically distributed, intermittently available end-users, often over under-provisioned connections. Such end-user data services are typically performed using point-to-point transfers that are designed for well-endowed sites and are unable to reconcile the center's resource usage and users delivery deadlines, unable to adapt to changing dynamics in the end-to-end data path and are not fault-tolerant. To overcome these inefficiencies, decentralized HPC data services are emerging as viable alternatives. In this paper, we develop and enhance such distributed data services by designing CATCH, a Cloud-based Adaptive data Transfer serviCe for HPC. CATCH leverages a bevy of cloud storage resources to orchestrate a decentralized data transport with fail-over capabilities. Our results demonstrate that CATCH is a feasible approach, and can help improve the data transfer times at the HPC center by as much as 81.1% for typical HPC workloads.
99 GENERAL AND MISCELLANEOUS↗