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.
Scalable Data Resilience for In-memory Data Staging
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
- Award ID(s):
- 1725649
- NSF-PAR ID:
- 10062928
- Date Published:
- Journal Name:
- Proceedings - IEEE International Parallel and Distributed Processing Symposium
- ISSN:
- 1530-2075
- Page Range / eLocation ID:
- 105-115
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Summary -
Scientific workflows drive most modern large-scale science breakthroughs by allowing scientists to define their computations as a set of jobs executed in a given order based on their data dependencies. Workflow management systems (WMSs) have become key to automating scientific workflows-executing computational jobs and orchestrating data transfers between those jobs running on complex high-performance computing (HPC) platforms. Traditionally, WMSs use files to communicate between jobs: a job writes out files that are read by other jobs. However, HPC machines face a growing gap between their storage and compute capabilities. To address that concern, the scientific community has adopted a new approach called in situ, which bypasses costly parallel filesystem I/O operations with faster in-memory or in-network communications. When using in situ approaches, communication and computations can be interleaved. In this work, we leverage the Decaf in situ dataflow framework to accelerate task-based scientific workflows managed by the Pegasus WMS, by replacing file communications with faster MPI messaging. We propose a new execution engine that uses Decaf to manage communications within a sub-workflow (i.e., set of jobs) to optimize inter-job communications. We consider two workflows in this study: (i) a synthetic workflow that benchmarks and compares file- and MPI-based communication; and (ii) a realistic bioinformatics workflow that computes mu-tational overlaps in the human genome. Experiments show that in situ communication can improve the bioinformatics workflow execution time by 22% to 30% compared with file communication. Our results motivate further opportunities and challenges for bridging traditional WMSs with in situ frameworks.more » « less
-
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing systems are pushing the frontiers of performance with a rich diversity of hardware resources and massive scale-out capabilities. There is a critical need to understand fair and effective benchmarking of machine learning applications that are representative of real-world scientific use cases. MLPerf ™ is a community-driven standard to benchmark machine learning workloads, focusing on end-to-end performance metrics. In this paper, we introduce MLPerf HPC, a benchmark suite of large-scale scientific machine learning training applications, driven by the MLCommons ™ Association. We present the results from the first submission round including a diverse set of some of the world’s largest HPC systems. We develop a systematic framework for their joint analysis and compare them in terms of data staging, algorithmic convergence and compute performance. As a result, we gain a quantitative understanding of optimizations on different subsystems such as staging and on-node loading of data, compute-unit utilization and communication scheduling enabling overall >10× (end-to-end) performance improvements through system scaling. Notably, our analysis shows a scale-dependent interplay between the dataset size, a system’s memory hierarchy and training convergence that underlines the importance of near-compute storage. To overcome the data-parallel scalability challenge at large batch-sizes, we discuss specific learning techniques and hybrid data-and-model parallelism that are effective on large systems. We conclude by characterizing each benchmark with respect to low-level memory, I/O and network behaviour to parameterize extended roofline performance models in future rounds.more » « less
-
Parallel I/O is an effective method to optimize data movement between memory and storage for many scientific applications. Poor performance of traditional disk-based file systems has led to the design of I/O libraries which take advantage of faster memory layers, such as on-node memory, present in high-performance computing (HPC) systems. By allowing caching and prefetching of data for applications alternating computation and I/O phases, a faster memory layer also provides opportunities for hiding the latency of I/O phases by overlapping them with computation phases, a technique called asynchronous I/O. Since asynchronous parallel I/O in HPC systems is still in the initial stages of development, there hasn't been a systematic study of the factors affecting its performance.In this paper, we perform a systematic study of various factors affecting the performance and efficacy of asynchronous I/O, we develop a performance model to estimate the aggregate I/O bandwidth achievable by iterative applications using synchronous and asynchronous I/O based on past observations, and we evaluate the performance of the recently developed asynchronous I/O feature of a parallel I/O library (HDF5) using benchmarks and real-world science applications. Our study covers parallel file systems on two large-scale HPC systems: Summit and Cori, the former with a GPFS storage and the latter with a Lustre parallel file system.more » « less
-
Emulating a shared atomic, read/write storage system is a fundamental problem in distributed computing. Replicating atomic objects among a set of data hosts was the norm for traditional implementations (e.g., [ 11]) in order to guarantee the availability and accessibility of the data despite host failures. As replication is highly storage demanding, recent approaches suggested the use of erasure-codes to offer the same fault-tolerance while optimizing storage usage at the hosts. Initial works focused on a fix set of data hosts. To guarantee longevity and scalability, a storage service should be able to dynamically mask hosts failures by allowing new hosts to join, and failed host to be removed without service interruptions. This work presents the first erasure-code based atomic algorithm, called ARES, which allows the set of hosts to be modified in the course of an execution. ARES is composed of three main components: (i) a reconfiguration protocol, (ii) a read/write protocol, and (iii) a set of data access primitives. The design of ARES is modular and is such to accommodate the usage of various erasure-code parameters on a per-configuration basis. We provide bounds on the latency of read/write operations, and analyze the storage and communication costs of the ARES algorithm.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IPDPS.2018.00021
