The dramatic increase in the scale of current and planned high-end HPC systems is leading new challenges, such as the growing costs of data movement and IO, and the reduced mean times between failures (MTBF) of system components. In- situ workflows, i.e., executing the entire application workflows on the HPC system, have emerged as an attractive approach to address data-related challenges by moving computations closer to the data, and staging-based frameworks have been effectively used to support in-situ workflows at scale. However, the resilience of these staging-based solutions has not been addressed and they remain susceptible to expensive data failures. Furthermore, naive use of data resilience techniques such as n-way replication and erasure codes can impact latency and/or result in significant storage overheads. In this paper, we present CoREC, a scalable resilient in-memory data staging runtime for large-scale in-situ workflows. CoREC uses a novel hybrid approach that combines dynamic replication with erasure coding based on data access patterns. The paper also presents optimizations for load balancing and conflict avoiding encoding, and a low overhead, lazy data recovery scheme. We have implemented the CoREC runtime and have deployed with the DataSpaces staging service on Titan at ORNL, and present an experimental evaluation in the paper. The experiments demonstrate that CoREC can tolerate in-memory data failures while maintaining low latency and sustaining high overall storage efficiency at large scales.
more »
« less
Persistent Data Staging Services for Data Intensive In-situ Scientific Workflows
Scientific simulation workflows executing on very large scale computing systems are essential modalities for scientific investigation. The increasing scales and resolution of these simulations provide new opportunities for accurately modeling complex natural and engineered phenomena. However, the increasing complexity necessitates managing, transporting, and processing unprecedented amounts of data, and as a result, researchers are increasingly exploring data-staging and in-situ workflows to reduce data movement and data-related overheads. However, as these workflows become more dynamic in their structures and behaviors, data staging and in-situ solutions must evolve to support new requirements.
In this paper, we explore how the service-oriented concept can be applied to extreme-scale in-situ workflows. Specifically, we explore persistent data staging as a service and present the design and implementation of DataSpaces as a Service, a service-oriented data staging framework. We use a dynamically coupled fusion simulation workflow to illustrate the capabilities of this framework and evaluate its performance and scalability.
more »
« less
- NSF-PAR ID:
- 10077384
- Date Published:
- Journal Name:
- Proceedings of the ACM International Workshop on Data-Intensive Distributed Computing
- Page Range / eLocation ID:
- 37 - 44
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Summary Coupled scientific simulation workflows are composed of heterogeneous component applications that simulate different aspects of the physical phenomena being modeled and that interact and exchange significant volumes of data at runtime. As the data volumes and generation rates keep growing, the traditional disk I/O–based data movement approach becomes cost prohibitive, and workflow requires more scalable and efficient approach to support the data movement. Moreover, the cost of moving large volume of data over system interconnection network becomes dominating and significantly impacts the workflow execution time. Minimize the amount of network data movement and localize data transfers are critical for reducing such cost. To achieve this, workflow task placement should exploit data locality to the extent possible and move computation closer to data. In this paper, we investigate applying in‐memory data staging and data‐centric task placement to reduce the data movement cost in large‐scale coupled simulation workflows. Specifically, we present a distributed data sharing and task execution framework that (1) co‐locates in‐memory data staging on application compute nodes to store data that needs to be shared or exchanged and (2) uses data‐centric task placement to map computations onto processor cores that a large portion of the data exchanges can be performed using the intra‐node shared memory. We also present the implementation of the framework and its experimental evaluation on Titan Cray XK7 petascale supercomputer.
-
more » « less
Summary Nowadays, we have entered the era of big data. In the area of high performance computing, large‐scale simulations can generate huge amounts of data with potentially critical information. However, these data are usually saved in intermediate files and are not instantly visible until advanced data analytics techniques are applied after reading all simulation data from persistent storages (eg, local disks or a parallel file system). This approach puts users in a situation where they spend long time on waiting for running simulations while not knowing the status of the running job. In this paper, we build a new computational framework to couple scientific simulations with multi‐step machine learning processes and in‐situ data visualizations. We also design a new scalable simulation‐time clustering algorithm to automatically detect fluid flow anomalies. This computational framework is built upon different software components and provides plug‐in data analysis and visualization functions over complex scientific workflows. With this advanced framework, users can monitor and get real‐time notifications of special patterns or anomalies from ongoing extreme‐scale turbulent flow simulations.
-
Scientific breakthroughs in biomolecular methods and improvements in hardware technology have shifted from a single long-running simulation to a large set of shorter simulations running simultaneously, called an ensemble. In an ensemble, each independent simulation is usually coupled with several analyses that apply identical or distinct algorithms on data produced by the corresponding simulation. Today, In situ methods are used to analyze large volumes of data generated by scientific simulations at runtime. This work studies the execution of ensemble-based simulations paired with In situ analyses using in-memory staging methods. Because simulations and analyses forming an ensemble typically run concurrently, deploying an ensemble requires efficient co-location-aware strategies, making sure the data flow between simulations and analyses that form an In situ workflow is efficient. Using an ensemble of molecular dynamics In situ workflows with multiple simulations and analyses, we first show that collecting traditional metrics such as makespan, instructions per cycle, memory usage, or cache miss ratio is not sufficient to characterize the complex behaviors of ensembles. Thus, we propose a method to evaluate the performance of ensembles of workflows that captures resource usage (efficiency), resource allocation, and component placement. Experimental results demonstrate that our proposed method can effectively capture the performance of different component placements in an ensemble. By evaluating different co-location scenarios, our performance indicator demonstrates improvements of up to four orders of magnitude when co-locating simulation and coupled analyses within a single computational host.more » « less
-
Scientific breakthroughs in biomolecular methods and improvements in hardware technology have shifted from a long-running simulation to a large set of shorter simulations running simultaneously, called an ensemble. In an ensemble, simulations are usually coupled with analyses of data produced by the simulations. In situ methods can be used to analyze large volumes of data generated by scientific simulations at runtime (i.e., simulations and analyses are performed concurrently). In this work, we study the execution of ensemble-based simulations paired with in situ analyses using in-memory staging methods. Using an ensemble of molecular dynamics in situ workflows with multiple simulations and analyses, we first show that collecting traditional metrics such as makespan, instructions per cycle, memory usage, or cache miss ratio is not sufficient to characterize complex behaviors of ensembles. We propose a method to evaluate the performance of ensembles of workflows that captures multiple resource usage aspects: resource efficiency, resource allocation, and resource provisioning. Experimental results demonstrate that the proposed method can effectively distinguish the performance of different component placements in an ensemble with up to 32 ensemble members. By evaluating different co-location scenarios, our proposed performance indicators demonstrate benefits of co-locating simulation and coupled analyses within a compute node.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/2912152.2912157
