5,285 research outputs found
ASCR/HEP Exascale Requirements Review Report
This draft report summarizes and details the findings, results, and
recommendations derived from the ASCR/HEP Exascale Requirements Review meeting
held in June, 2015. The main conclusions are as follows. 1) Larger, more
capable computing and data facilities are needed to support HEP science goals
in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of
the demand at the 2025 timescale is at least two orders of magnitude -- and in
some cases greater -- than that available currently. 2) The growth rate of data
produced by simulations is overwhelming the current ability, of both facilities
and researchers, to store and analyze it. Additional resources and new
techniques for data analysis are urgently needed. 3) Data rates and volumes
from HEP experimental facilities are also straining the ability to store and
analyze large and complex data volumes. Appropriately configured
leadership-class facilities can play a transformational role in enabling
scientific discovery from these datasets. 4) A close integration of HPC
simulation and data analysis will aid greatly in interpreting results from HEP
experiments. Such an integration will minimize data movement and facilitate
interdependent workflows. 5) Long-range planning between HEP and ASCR will be
required to meet HEP's research needs. To best use ASCR HPC resources the
experimental HEP program needs a) an established long-term plan for access to
ASCR computational and data resources, b) an ability to map workflows onto HPC
resources, c) the ability for ASCR facilities to accommodate workflows run by
collaborations that can have thousands of individual members, d) to transition
codes to the next-generation HPC platforms that will be available at ASCR
facilities, e) to build up and train a workforce capable of developing and
using simulations and analysis to support HEP scientific research on
next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio
Deferred lightweight indexing for log-structured key-value stores
The recent shift towards write-intensive workload on bigdata (e.g., financial trading, social user-generated data streams)has pushed the proliferation of log-structured key-value stores, represented by Google's BigTable [1], Apache HBase [2] andCassandra [3]. While providing key-based data access with aPut/Get interface, these key-value stores do not support value-based access methods, which significantly limits their applicability in modern web and database applications. In this paper, we present DELI, a DEferred Lightweight Indexing scheme on the log-structured key-value stores. To index intensively updated bigdata in real time, DELI aims at making the index maintenance as lightweight as possible. The key idea is to apply an append-only design for online index maintenance and to collect index garbage at carefully chosen time. DELI optimizes the performance of index garbage collection through tightly coupling its execution with a native routine process called compaction. The DELI'ssystem design is fault-tolerant and generic (to most key-valuestores), we implemented a prototype of DELI based on HBasewithout internal code modification. Our experiments show that the DELI offers significant performance advantage for the write-intensive index maintenance
Honeycomb: ordered key-value store acceleration on an FPGA-based SmartNIC
In-memory ordered key-value stores are an important building block in modern
distributed applications. We present Honeycomb, a hybrid software-hardware
system for accelerating read-dominated workloads on ordered key-value stores
that provides linearizability for all operations including scans. Honeycomb
stores a B-Tree in host memory, and executes SCAN and GET on an FPGA-based
SmartNIC, and PUT, UPDATE and DELETE on the CPU. This approach enables large
stores and simplifies the FPGA implementation but raises the challenge of data
access and synchronization across the slow PCIe bus. We describe how Honeycomb
overcomes this challenge with careful data structure design, caching, request
parallelism with out-of-order request execution, wait-free read operations, and
batching synchronization between the CPU and the FPGA. For read-heavy YCSB
workloads, Honeycomb improves the throughput of a state-of-the-art ordered
key-value store by at least 1.8x. For scan-heavy workloads inspired by cloud
storage, Honeycomb improves throughput by more than 2x. The cost-performance,
which is more important for large-scale deployments, is improved by at least
1.5x on these workloads
Using Java for distributed computing in the Gaia satellite data processing
In recent years Java has matured to a stable easy-to-use language with the
flexibility of an interpreter (for reflection etc.) but the performance and
type checking of a compiled language. When we started using Java for
astronomical applications around 1999 they were the first of their kind in
astronomy. Now a great deal of astronomy software is written in Java as are
many business applications.
We discuss the current environment and trends concerning the language and
present an actual example of scientific use of Java for high-performance
distributed computing: ESA's mission Gaia. The Gaia scanning satellite will
perform a galactic census of about 1000 million objects in our galaxy. The Gaia
community has chosen to write its processing software in Java. We explore the
manifold reasons for choosing Java for this large science collaboration.
Gaia processing is numerically complex but highly distributable, some parts
being embarrassingly parallel. We describe the Gaia processing architecture and
its realisation in Java. We delve into the astrometric solution which is the
most advanced and most complex part of the processing. The Gaia simulator is
also written in Java and is the most mature code in the system. This has been
successfully running since about 2005 on the supercomputer "Marenostrum" in
Barcelona. We relate experiences of using Java on a large shared machine.
Finally we discuss Java, including some of its problems, for scientific
computing.Comment: Experimental Astronomy, August 201
- …