71,344 research outputs found
Towards Energy Consumption Verification via Static Analysis
In this paper we leverage an existing general framework for resource usage
verification and specialize it for verifying energy consumption specifications
of embedded programs. Such specifications can include both lower and upper
bounds on energy usage, and they can express intervals within which energy
usage is to be certified to be within such bounds. The bounds of the intervals
can be given in general as functions on input data sizes. Our verification
system can prove whether such energy usage specifications are met or not. It
can also infer the particular conditions under which the specifications hold.
To this end, these conditions are also expressed as intervals of functions of
input data sizes, such that a given specification can be proved for some
intervals but disproved for others. The specifications themselves can also
include preconditions expressing intervals for input data sizes. We report on a
prototype implementation of our approach within the CiaoPP system for the XC
language and XS1-L architecture, and illustrate with an example how embedded
software developers can use this tool, and in particular for determining values
for program parameters that ensure meeting a given energy budget while
minimizing the loss in quality of service.Comment: Presented at HIP3ES, 2015 (arXiv: 1501.03064
Symbolic and analytic techniques for resource analysis of Java bytecode
Recent work in resource analysis has translated the idea of amortised resource analysis to imperative languages using a program logic that allows mixing of assertions about heap shapes, in the tradition of separation logic, and assertions about consumable resources. Separately, polyhedral methods have been used to calculate bounds on numbers of iterations in loop-based programs. We are attempting to combine these ideas to deal with Java programs involving both data structures and loops, focusing on the bytecode level rather than on source code
Optimizing I/O for Big Array Analytics
Big array analytics is becoming indispensable in answering important
scientific and business questions. Most analysis tasks consist of multiple
steps, each making one or multiple passes over the arrays to be analyzed and
generating intermediate results. In the big data setting, I/O optimization is a
key to efficient analytics. In this paper, we develop a framework and
techniques for capturing a broad range of analysis tasks expressible in
nested-loop forms, representing them in a declarative way, and optimizing their
I/O by identifying sharing opportunities. Experiment results show that our
optimizer is capable of finding execution plans that exploit nontrivial I/O
sharing opportunities with significant savings.Comment: VLDB201
Eliminating Network Protocol Vulnerabilities Through Abstraction and Systems Language Design
Incorrect implementations of network protocol message specifications affect
the stability, security, and cost of network system development. Most
implementation defects fall into one of three categories of well defined
message constraints. However, the general process of constructing network
protocol stacks and systems does not capture these categorical con- straints.
We introduce a systems programming language with new abstractions that capture
these constraints. Safe and efficient implementations of standard message
handling operations are synthesized by our compiler, and whole-program analysis
is used to ensure constraints are never violated. We present language examples
using the OpenFlow protocol
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
An Introduction to Programming for Bioscientists: A Python-based Primer
Computing has revolutionized the biological sciences over the past several
decades, such that virtually all contemporary research in the biosciences
utilizes computer programs. The computational advances have come on many
fronts, spurred by fundamental developments in hardware, software, and
algorithms. These advances have influenced, and even engendered, a phenomenal
array of bioscience fields, including molecular evolution and bioinformatics;
genome-, proteome-, transcriptome- and metabolome-wide experimental studies;
structural genomics; and atomistic simulations of cellular-scale molecular
assemblies as large as ribosomes and intact viruses. In short, much of
post-genomic biology is increasingly becoming a form of computational biology.
The ability to design and write computer programs is among the most
indispensable skills that a modern researcher can cultivate. Python has become
a popular programming language in the biosciences, largely because (i) its
straightforward semantics and clean syntax make it a readily accessible first
language; (ii) it is expressive and well-suited to object-oriented programming,
as well as other modern paradigms; and (iii) the many available libraries and
third-party toolkits extend the functionality of the core language into
virtually every biological domain (sequence and structure analyses,
phylogenomics, workflow management systems, etc.). This primer offers a basic
introduction to coding, via Python, and it includes concrete examples and
exercises to illustrate the language's usage and capabilities; the main text
culminates with a final project in structural bioinformatics. A suite of
Supplemental Chapters is also provided. Starting with basic concepts, such as
that of a 'variable', the Chapters methodically advance the reader to the point
of writing a graphical user interface to compute the Hamming distance between
two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables,
numerous exercises, and 19 pages of Supporting Information; currently in
press at PLOS Computational Biolog
A Compiler and Runtime Infrastructure for Automatic Program Distribution
This paper presents the design and the implementation of a compiler and runtime infrastructure for automatic program distribution. We are building a research infrastructure that enables experimentation with various program partitioning and mapping strategies and the study of automatic distribution's effect on resource consumption (e.g., CPU, memory, communication). Since many optimization techniques are faced with conflicting optimization targets (e.g., memory and communication), we believe that it is important to be able to study their interaction.
We present a set of techniques that enable flexible resource modeling and program distribution. These are: dependence analysis, weighted graph partitioning, code and communication generation, and profiling. We have developed these ideas in the context of the Java language. We present in detail the design and implementation of each of the techniques as part of our compiler and runtime infrastructure. Then, we evaluate our design and present preliminary experimental data for each component, as well as for the entire system
Telescope Bibliographies: an Essential Component of Archival Data Management and Operations
Assessing the impact of astronomical facilities rests upon an evaluation of
the scientific discoveries which their data have enabled. Telescope
bibliographies, which link data products with the literature, provide a way to
use bibliometrics as an impact measure for the underlying data. In this paper
we argue that the creation and maintenance of telescope bibliographies should
be considered an integral part of an observatory's operations. We review the
existing tools, services, and workflows which support these curation
activities, giving an estimate of the effort and expertise required to maintain
an archive-based telescope bibliography.Comment: 10 pages, 3 figures, to appear in SPIE Astronomical Telescopes and
Instrumentation, SPIE Conference Series 844
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