6,651 research outputs found
BEEBS: Open Benchmarks for Energy Measurements on Embedded Platforms
This paper presents and justifies an open benchmark suite named BEEBS,
targeted at evaluating the energy consumption of embedded processors.
We explore the possible sources of energy consumption, then select individual
benchmarks from contemporary suites to cover these areas. Version one of BEEBS
is presented here and contains 10 benchmarks that cover a wide range of typical
embedded applications. The benchmark suite is portable across diverse
architectures and is freely available.
The benchmark suite is extensively evaluated, and the properties of its
constituent programs are analysed. Using real hardware platforms we show case
examples which illustrate the difference in power dissipation between three
processor architectures and their related ISAs. We observe significant
differences in the average instruction dissipation between the architectures of
4.4x, specifically 170uW/MHz (ARM Cortex-M0), 65uW/MHz (Adapteva Epiphany) and
88uW/MHz (XMOS XS1-L1)
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 brieļ¬y discuss the implications of our ļ¬ndings for improving
the performance of JIT compilation and garbage collection on the
JVM platform
HAPPY: Hybrid Address-based Page Policy in DRAMs
Memory controllers have used static page closure policies to decide whether a
row should be left open, open-page policy, or closed immediately, close-page
policy, after the row has been accessed. The appropriate choice for a
particular access can reduce the average memory latency. However, since
application access patterns change at run time, static page policies cannot
guarantee to deliver optimum execution time. Hybrid page policies have been
investigated as a means of covering these dynamic scenarios and are now
implemented in state-of-the-art processors. Hybrid page policies switch between
open-page and close-page policies while the application is running, by
monitoring the access pattern of row hits/conflicts and predicting future
behavior. Unfortunately, as the size of DRAM memory increases, fine-grain
tracking and analysis of memory access patterns does not remain practical. We
propose a compact memory address-based encoding technique which can improve or
maintain the performance of DRAMs page closure predictors while reducing the
hardware overhead in comparison with state-of-the-art techniques. As a case
study, we integrate our technique, HAPPY, with a state-of-the-art monitor, the
Intel-adaptive open-page policy predictor employed by the Intel Xeon X5650, and
a traditional Hybrid page policy. We evaluate them across 70 memory intensive
workload mixes consisting of single-thread and multi-thread applications. The
experimental results show that using the HAPPY encoding applied to the
Intel-adaptive page closure policy can reduce the hardware overhead by 5X for
the evaluated 64 GB memory (up to 40X for a 512 GB memory) while maintaining
the prediction accuracy
Correcting pervasive errors in RNA crystallography through enumerative structure prediction
Three-dimensional RNA models fitted into crystallographic density maps
exhibit pervasive conformational ambiguities, geometric errors and steric
clashes. To address these problems, we present enumerative real-space
refinement assisted by electron density under Rosetta (ERRASER), coupled to
Python-based hierarchical environment for integrated 'xtallography' (PHENIX)
diffraction-based refinement. On 24 data sets, ERRASER automatically corrects
the majority of MolProbity-assessed errors, improves the average Rfree factor,
resolves functionally important discrepancies in noncanonical structure and
refines low-resolution models to better match higher-resolution models
Automatic Repair of Real Bugs: An Experience Report on the Defects4J Dataset
Defects4J is a large, peer-reviewed, structured dataset of real-world Java
bugs. Each bug in Defects4J is provided with a test suite and at least one
failing test case that triggers the bug. In this paper, we report on an
experiment to explore the effectiveness of automatic repair on Defects4J. The
result of our experiment shows that 47 bugs of the Defects4J dataset can be
automatically repaired by state-of- the-art repair. This sets a baseline for
future research on automatic repair for Java. We have manually analyzed 84
different patches to assess their real correctness. In total, 9 real Java bugs
can be correctly fixed with test-suite based repair. This analysis shows that
test-suite based repair suffers from under-specified bugs, for which trivial
and incorrect patches still pass the test suite. With respect to practical
applicability, it takes in average 14.8 minutes to find a patch. The experiment
was done on a scientific grid, totaling 17.6 days of computation time. All
their systems and experimental results are publicly available on Github in
order to facilitate future research on automatic repair
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