108 research outputs found
Performance Measurements of Supercomputing and Cloud Storage Solutions
Increasing amounts of data from varied sources, particularly in the fields of
machine learning and graph analytics, are causing storage requirements to grow
rapidly. A variety of technologies exist for storing and sharing these data,
ranging from parallel file systems used by supercomputers to distributed block
storage systems found in clouds. Relatively few comparative measurements exist
to inform decisions about which storage systems are best suited for particular
tasks. This work provides these measurements for two of the most popular
storage technologies: Lustre and Amazon S3. Lustre is an open-source, high
performance, parallel file system used by many of the largest supercomputers in
the world. Amazon's Simple Storage Service, or S3, is part of the Amazon Web
Services offering, and offers a scalable, distributed option to store and
retrieve data from anywhere on the Internet. Parallel processing is essential
for achieving high performance on modern storage systems. The performance tests
used span the gamut of parallel I/O scenarios, ranging from single-client,
single-node Amazon S3 and Lustre performance to a large-scale, multi-client
test designed to demonstrate the capabilities of a modern storage appliance
under heavy load. These results show that, when parallel I/O is used correctly
(i.e., many simultaneous read or write processes), full network bandwidth
performance is achievable and ranged from 10 gigabits/s over a 10 GigE S3
connection to 0.35 terabits/s using Lustre on a 1200 port 10 GigE switch. These
results demonstrate that S3 is well-suited to sharing vast quantities of data
over the Internet, while Lustre is well-suited to processing large quantities
of data locally.Comment: 5 pages, 4 figures, to appear in IEEE HPEC 201
Lustre, Hadoop, Accumulo
Data processing systems impose multiple views on data as it is processed by
the system. These views include spreadsheets, databases, matrices, and graphs.
There are a wide variety of technologies that can be used to store and process
data through these different steps. The Lustre parallel file system, the Hadoop
distributed file system, and the Accumulo database are all designed to address
the largest and the most challenging data storage problems. There have been
many ad-hoc comparisons of these technologies. This paper describes the
foundational principles of each technology, provides simple models for
assessing their capabilities, and compares the various technologies on a
hypothetical common cluster. These comparisons indicate that Lustre provides 2x
more storage capacity, is less likely to loose data during 3 simultaneous drive
failures, and provides higher bandwidth on general purpose workloads. Hadoop
can provide 4x greater read bandwidth on special purpose workloads. Accumulo
provides 10,000x lower latency on random lookups than either Lustre or Hadoop
but Accumulo's bulk bandwidth is 10x less. Significant recent work has been
done to enable mix-and-match solutions that allow Lustre, Hadoop, and Accumulo
to be combined in different ways.Comment: 6 pages; accepted to IEEE High Performance Extreme Computing
conference, Waltham, MA, 201
Enabling On-Demand Database Computing with MIT SuperCloud Database Management System
The MIT SuperCloud database management system allows for rapid creation and
flexible execution of a variety of the latest scientific databases, including
Apache Accumulo and SciDB. It is designed to permit these databases to run on a
High Performance Computing Cluster (HPCC) platform as seamlessly as any other
HPCC job. It ensures the seamless migration of the databases to the resources
assigned by the HPCC scheduler and centralized storage of the database files
when not running. It also permits snapshotting of databases to allow
researchers to experiment and push the limits of the technology without
concerns for data or productivity loss if the database becomes unstable.Comment: 6 pages; accepted to IEEE High Performance Extreme Computing (HPEC)
conference 2015. arXiv admin note: text overlap with arXiv:1406.492
Lessons Learned from a Decade of Providing Interactive, On-Demand High Performance Computing to Scientists and Engineers
For decades, the use of HPC systems was limited to those in the physical
sciences who had mastered their domain in conjunction with a deep understanding
of HPC architectures and algorithms. During these same decades, consumer
computing device advances produced tablets and smartphones that allow millions
of children to interactively develop and share code projects across the globe.
As the HPC community faces the challenges associated with guiding researchers
from disciplines using high productivity interactive tools to effective use of
HPC systems, it seems appropriate to revisit the assumptions surrounding the
necessary skills required for access to large computational systems. For over a
decade, MIT Lincoln Laboratory has been supporting interactive, on-demand high
performance computing by seamlessly integrating familiar high productivity
tools to provide users with an increased number of design turns, rapid
prototyping capability, and faster time to insight. In this paper, we discuss
the lessons learned while supporting interactive, on-demand high performance
computing from the perspectives of the users and the team supporting the users
and the system. Building on these lessons, we present an overview of current
needs and the technical solutions we are building to lower the barrier to entry
for new users from the humanities, social, and biological sciences.Comment: 15 pages, 3 figures, First Workshop on Interactive High Performance
Computing (WIHPC) 2018 held in conjunction with ISC High Performance 2018 in
Frankfurt, German
Measuring the Impact of Spectre and Meltdown
The Spectre and Meltdown flaws in modern microprocessors represent a new
class of attacks that have been difficult to mitigate. The mitigations that
have been proposed have known performance impacts. The reported magnitude of
these impacts varies depending on the industry sector and expected workload
characteristics. In this paper, we measure the performance impact on several
workloads relevant to HPC systems. We show that the impact can be significant
on both synthetic and realistic workloads. We also show that the performance
penalties are difficult to avoid even in dedicated systems where security is a
lesser concern
Benchmarking SciDB Data Import on HPC Systems
SciDB is a scalable, computational database management system that uses an
array model for data storage. The array data model of SciDB makes it ideally
suited for storing and managing large amounts of imaging data. SciDB is
designed to support advanced analytics in database, thus reducing the need for
extracting data for analysis. It is designed to be massively parallel and can
run on commodity hardware in a high performance computing (HPC) environment. In
this paper, we present the performance of SciDB using simulated image data. The
Dynamic Distributed Dimensional Data Model (D4M) software is used to implement
the benchmark on a cluster running the MIT SuperCloud software stack. A peak
performance of 2.2M database inserts per second was achieved on a single node
of this system. We also show that SciDB and the D4M toolbox provide more
efficient ways to access random sub-volumes of massive datasets compared to the
traditional approaches of reading volumetric data from individual files. This
work describes the D4M and SciDB tools we developed and presents the initial
performance results. This performance was achieved by using parallel inserts, a
in-database merging of arrays as well as supercomputing techniques, such as
distributed arrays and single-program-multiple-data programming.Comment: 5 pages, 4 figures, IEEE High Performance Extreme Computing (HPEC)
2016, best paper finalis
Achieving 100,000,000 database inserts per second using Accumulo and D4M
The Apache Accumulo database is an open source relaxed consistency database
that is widely used for government applications. Accumulo is designed to
deliver high performance on unstructured data such as graphs of network data.
This paper tests the performance of Accumulo using data from the Graph500
benchmark. The Dynamic Distributed Dimensional Data Model (D4M) software is
used to implement the benchmark on a 216-node cluster running the MIT
SuperCloud software stack. A peak performance of over 100,000,000 database
inserts per second was achieved which is 100x larger than the highest
previously published value for any other database. The performance scales
linearly with the number of ingest clients, number of database servers, and
data size. The performance was achieved by adapting several supercomputing
techniques to this application: distributed arrays, domain decomposition,
adaptive load balancing, and single-program-multiple-data programming.Comment: 6 pages; to appear in IEEE High Performance Extreme Computing (HPEC)
201
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