As large-scale scientific simulations and big data analyses become
more popular, it is increasingly more expensive to store huge
amounts of raw simulation results to perform post-analysis. To
minimize the expensive data I/O, “in-situ” analysis is a promising
approach, where data analysis applications analyze the simulation
generated data on the fly without storing it first. However, it is
challenging to organize, transform, and transport data at scales
between two semantically different ecosystems due to the distinct
software and hardware difference. To tackle these challenges, we
design and implement the X-Composer framework. X-Composer
connects cross-ecosystem applications to form an “in-situ” scientific
workflow, and provides a unified approach and recipe for supporting
such hybrid in-situ workflows on distributed heterogeneous
resources. X-Composer reorganizes simulation data as continuous
data streams and feeds them seamlessly into the Cloud-based
stream processing services to minimize I/O overheads. For evaluation,
we use X-Composer to set up and execute a cross-ecosystem
workflow, which consists of a parallel Computational Fluid Dynamics
simulation running on HPC, and a distributed Dynamic
Mode Decomposition analysis application running on Cloud. Our
experimental results show that X-Composer can seamlessly couple
HPC and Big Data jobs in their own native environments, achieve
good scalability, and provide high-fidelity analytics for ongoing
simulations in real-time.
more »
« less
Accelerating Scientific Workflows on HPC Platforms with In Situ Processing
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
- NSF-PAR ID:
- 10355276
- Date Published:
- Journal Name:
- 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Biscarat, C. ; Campana, S. ; Hegner, B. ; Roiser, S. ; Rovelli, C.I. ; Stewart, G.A. (Ed.)CMS is tackling the exploitation of CPU resources at HPC centers where compute nodes do not have network connectivity to the Internet. Pilot agents and payload jobs need to interact with external services from the compute nodes: access to the application software (CernVM-FS) and conditions data (Frontier), management of input and output data files (data management services), and job management (HTCondor). Finding an alternative route to these services is challenging. Seamless integration in the CMS production system without causing any operational overhead is a key goal. The case of the Barcelona Supercomputing Center (BSC), in Spain, is particularly challenging, due to its especially restrictive network setup. We describe in this paper the solutions developed within CMS to overcome these restrictions, and integrate this resource in production. Singularity containers with application software releases are built and pre-placed in the HPC facility shared file system, together with conditions data files. HTCondor has been extended to relay communications between running pilot jobs and HTCondor daemons through the HPC shared file system. This operation mode also allows piping input and output data files through the HPC file system. Results, issues encountered during the integration process, and remaining concerns are discussed.more » « less
-
Workflow management systems (WMSs) are commonly used to organize/automate sequences of tasks as workflows to accelerate scientific discoveries. During complex workflow modeling, a local interactive workflow environment is desirable, as users usually rely on their rich, local environments for fast prototyping and refinements before they consider using more powerful computing resources. However, existing WMSs do not simultaneously support local interactive workflow environments and HPC resources. In this paper, we present an on-demand access mechanism to remote HPC resources from desktop/laptopbased workflow management software to compose, monitor and analyze scientific workflows in the CyberWater project. Cyber- Water is an open-data and open-modeling software framework for environmental and water communities. In this work, we extend the open-model, open-data design of CyberWater with on-demand HPC accessing capacity. In particular, we design and implement the LaunchAgent library, which can be integrated into the local desktop environment to allow on-demand usage of remote resources for hydrology-related workflows. LaunchAgent manages authentication to remote resources, prepares the computationally-intensive or data-intensive tasks as batch jobs, submits jobs to remote resources, and monitors the quality of services for the users. LaunchAgent interacts seamlessly with other existing components in CyberWater, which is now able to provide advantages of both feature-rich desktop software experience and increased computation power through on-demand HPC/Cloud usage. In our evaluations, we demonstrate how a hydrology workflow that consists of both local and remote tasks can be constructed and show that the added on-demand HPC/Cloud usage helps speeding up hydrology workflows while allowing intuitive workflow configurations and execution using a desktop graphical user interface.more » « less
-
DOI 10.1109/COMPSAC.2019.10284 Abstract—Hadoop is a popular data-analytics platform based on the MapReduce model. When analyzing extremely big data, hard disk drives are commonly used and Hadoop performance can be optimized by improving I/O performance. Hard disk drives have different performance depending on whether data are placed in the outer or inner disk zones. In this paper, we propose a method that uses knowledge of job characteristics to place data in hard disk drives so that Hadoop performance is improved. Files of a job that intensively and sequentially accesses the storage device are placed in outer disk tracks which have higher sequential access speed than inner tracks. Temporary and permanent files are placed in the outer and inner zones, respectively. This enables repeated usage of the faster zones by avoiding the use of the faster zones by permanent files. Our evaluation demonstrates that the proposed method improves the performance of Hadoop jobs by 15.0% over the normal case when file placement is not used. The proposed method also outperforms a previously proposed placement approach by 9.9%.more » « less
-
null (Ed.)Many scientific applications operate on large datasets that can be partitioned and operated on concurrently.The existing approaches for concurrent execution generally rely on statically partitioned data. This static partitioning can lock performance in a sub-optimal configuration, leading to higher execution time and an inability to respond to dynamic resources.We present the Continuously Divisible Job abstraction which allows statically defined applications to have their component tasks dynamically sized responding to system behaviour. The Continuously Divisible Job abstraction defines a simple interface that dictates how work can be recursively divided, executed,and merged. Implementing this abstraction allows scientific applications to leverage dynamic job coordinators for execution.We also propose the Virtual File abstraction which allows read-only subsets of large files to be treated as separate files.In exploring the Continuously Divisible Job abstraction, two applications were implemented using the Continuously Divisible Job interface: a bioinformatics application and a high-energy physics event analysis. These were tested using an abstract job interface and several job coordinators. Comparing these against a previous static partitioning implementation we show comparable or better performance without having to make static decisions or implement complex dynamic application handling.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CCGrid54584.2022.00009
