12,624 research outputs found
JVM-hosted languages: They talk the talk, but do they walk the walk?
The rapid adoption of non-Java JVM languages is impressive: major international corporations are staking critical parts of their software infrastructure on components built from languages such as
Scala and Clojure. However with the possible exception of Scala,
there has been little academic consideration and characterization
of these languages to date. In this paper, we examine four nonJava JVM languages and use exploratory data analysis techniques
to investigate differences in their dynamic behavior compared to
Java. We analyse a variety of programs and levels of behavior to
draw distinctions between the different programming languages.
We briefly discuss the implications of our findings for improving
the performance of JIT compilation and garbage collection on the
JVM platform
A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures
Scientific problems that depend on processing large amounts of data require
overcoming challenges in multiple areas: managing large-scale data
distribution, co-placement and scheduling of data with compute resources, and
storing and transferring large volumes of data. We analyze the ecosystems of
the two prominent paradigms for data-intensive applications, hereafter referred
to as the high-performance computing and the Apache-Hadoop paradigm. We propose
a basis, common terminology and functional factors upon which to analyze the
two approaches of both paradigms. We discuss the concept of "Big Data Ogres"
and their facets as means of understanding and characterizing the most common
application workloads found across the two paradigms. We then discuss the
salient features of the two paradigms, and compare and contrast the two
approaches. Specifically, we examine common implementation/approaches of these
paradigms, shed light upon the reasons for their current "architecture" and
discuss some typical workloads that utilize them. In spite of the significant
software distinctions, we believe there is architectural similarity. We discuss
the potential integration of different implementations, across the different
levels and components. Our comparison progresses from a fully qualitative
examination of the two paradigms, to a semi-quantitative methodology. We use a
simple and broadly used Ogre (K-means clustering), characterize its performance
on a range of representative platforms, covering several implementations from
both paradigms. Our experiments provide an insight into the relative strengths
of the two paradigms. We propose that the set of Ogres will serve as a
benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure
COST Action IC 1402 ArVI: Runtime Verification Beyond Monitoring -- Activity Report of Working Group 1
This report presents the activities of the first working group of the COST
Action ArVI, Runtime Verification beyond Monitoring. The report aims to provide
an overview of some of the major core aspects involved in Runtime Verification.
Runtime Verification is the field of research dedicated to the analysis of
system executions. It is often seen as a discipline that studies how a system
run satisfies or violates correctness properties. The report exposes a taxonomy
of Runtime Verification (RV) presenting the terminology involved with the main
concepts of the field. The report also develops the concept of instrumentation,
the various ways to instrument systems, and the fundamental role of
instrumentation in designing an RV framework. We also discuss how RV interplays
with other verification techniques such as model-checking, deductive
verification, model learning, testing, and runtime assertion checking. Finally,
we propose challenges in monitoring quantitative and statistical data beyond
detecting property violation
A Context-Oriented Extension of F#
Context-Oriented programming languages provide us with primitive constructs
to adapt program behaviour depending on the evolution of their operational
environment, namely the context. In previous work we proposed ML_CoDa, a
context-oriented language with two-components: a declarative constituent for
programming the context and a functional one for computing. This paper
describes the implementation of ML_CoDa as an extension of F#.Comment: In Proceedings FOCLASA 2015, arXiv:1512.0694
Developing a distributed electronic health-record store for India
The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India
DeepOBS: A Deep Learning Optimizer Benchmark Suite
Because the choice and tuning of the optimizer affects the speed, and
ultimately the performance of deep learning, there is significant past and
recent research in this area. Yet, perhaps surprisingly, there is no generally
agreed-upon protocol for the quantitative and reproducible evaluation of
optimization strategies for deep learning. We suggest routines and benchmarks
for stochastic optimization, with special focus on the unique aspects of deep
learning, such as stochasticity, tunability and generalization. As the primary
contribution, we present DeepOBS, a Python package of deep learning
optimization benchmarks. The package addresses key challenges in the
quantitative assessment of stochastic optimizers, and automates most steps of
benchmarking. The library includes a wide and extensible set of ready-to-use
realistic optimization problems, such as training Residual Networks for image
classification on ImageNet or character-level language prediction models, as
well as popular classics like MNIST and CIFAR-10. The package also provides
realistic baseline results for the most popular optimizers on these test
problems, ensuring a fair comparison to the competition when benchmarking new
optimizers, and without having to run costly experiments. It comes with output
back-ends that directly produce LaTeX code for inclusion in academic
publications. It supports TensorFlow and is available open source.Comment: Accepted at ICLR 2019. 9 pages, 3 figures, 2 table
Structural Analysis: Shape Information via Points-To Computation
This paper introduces a new hybrid memory analysis, Structural Analysis,
which combines an expressive shape analysis style abstract domain with
efficient and simple points-to style transfer functions. Using data from
empirical studies on the runtime heap structures and the programmatic idioms
used in modern object-oriented languages we construct a heap analysis with the
following characteristics: (1) it can express a rich set of structural, shape,
and sharing properties which are not provided by a classic points-to analysis
and that are useful for optimization and error detection applications (2) it
uses efficient, weakly-updating, set-based transfer functions which enable the
analysis to be more robust and scalable than a shape analysis and (3) it can be
used as the basis for a scalable interprocedural analysis that produces precise
results in practice.
The analysis has been implemented for .Net bytecode and using this
implementation we evaluate both the runtime cost and the precision of the
results on a number of well known benchmarks and real world programs. Our
experimental evaluations show that the domain defined in this paper is capable
of precisely expressing the majority of the connectivity, shape, and sharing
properties that occur in practice and, despite the use of weak updates, the
static analysis is able to precisely approximate the ideal results. The
analysis is capable of analyzing large real-world programs (over 30K bytecodes)
in less than 65 seconds and using less than 130MB of memory. In summary this
work presents a new type of memory analysis that advances the state of the art
with respect to expressive power, precision, and scalability and represents a
new area of study on the relationships between and combination of concepts from
shape and points-to analyses
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