191,525 research outputs found
A Structured Memory Access Architecture
Naval Electronics Systems Command VHSIC Program / N00039-80-C-0556Ope
Structured Access in Sentence Comprehension
This thesis is concerned with the nature of memory access during the construction of long-distance dependencies in online sentence comprehension. In recent years, an intense focus on the computational challenges posed by long-distance dependencies has proven to be illuminating with respect to the characteristics of the architecture of the human sentence processor, suggesting a tight link between general memory access procedures and sentence processing routines (Lewis & Vasishth 2005; Lewis, Vasishth, & Van Dyke 2006; Wagers, Lau & Phillips 2009). The present thesis builds upon this line of research, and its primary aim is to motivate and defend the hypothesis that the parser accesses linguistic memory in an essentially structured fashion for certain long-distance dependencies. In order to make this case, I focus on the processing of reflexive and agreement dependencies, and ask whether or not non-structural information such as morphological features are used to gate memory access during syntactic comprehension. Evidence from eight experiments in a range of methodologies in English and Chinese is brought to bear on this question, providing arguments from interference effects and time-course effects that primarily syntactic information is used to access linguistic memory in the construction of certain long-distance dependencies. The experimental evidence for structured access is compatible
with a variety of architectural assumptions about the parser, and I present one implementation of this idea in a parser based on the ACT-R memory architecture. In the context of such a content-addressable model of memory, the claim of structured access is equivalent to the claim that only syntactic cues are used to query memory. I argue that structured access reflects an optimal parsing strategy in the context of a noisy, interference-prone cognitive architecture: abstract structural cues are favored over lexical feature cues for certain structural dependencies in order to minimize memory interference in online processing
Updates on the Low-Level Abstraction of Memory Access
Choosing the best memory layout for each hardware architecture is
increasingly important as more and more programs become memory bound. For
portable codes that run across heterogeneous hardware architectures, the choice
of the memory layout for data structures is ideally decoupled from the rest of
a program. The low-level abstraction of memory access (LLAMA) is a C++ library
that provides a zero-runtime-overhead abstraction layer, underneath which
memory mappings can be freely exchanged to customize data layouts, memory
access and access instrumentation, focusing on multidimensional arrays of
nested, structured data.
After its scientific debut, several improvements and extensions have been
added to LLAMA. This includes compile-time array extents for
zero-memory-overhead views, support for computations during memory access, new
mappings for bit-packing, switching types, byte-splitting, memory access
instrumentation, and explicit SIMD support. This contribution provides an
overview of recent developments in the LLAMA library
BigDataBench: a Big Data Benchmark Suite from Internet Services
As architecture, systems, and data management communities pay greater
attention to innovative big data systems and architectures, the pressure of
benchmarking and evaluating these systems rises. Considering the broad use of
big data systems, big data benchmarks must include diversity of data and
workloads. Most of the state-of-the-art big data benchmarking efforts target
evaluating specific types of applications or system software stacks, and hence
they are not qualified for serving the purposes mentioned above. This paper
presents our joint research efforts on this issue with several industrial
partners. Our big data benchmark suite BigDataBench not only covers broad
application scenarios, but also includes diverse and representative data sets.
BigDataBench is publicly available from http://prof.ict.ac.cn/BigDataBench .
Also, we comprehensively characterize 19 big data workloads included in
BigDataBench with varying data inputs. On a typical state-of-practice
processor, Intel Xeon E5645, we have the following observations: First, in
comparison with the traditional benchmarks: including PARSEC, HPCC, and
SPECCPU, big data applications have very low operation intensity; Second, the
volume of data input has non-negligible impact on micro-architecture
characteristics, which may impose challenges for simulation-based big data
architecture research; Last but not least, corroborating the observations in
CloudSuite and DCBench (which use smaller data inputs), we find that the
numbers of L1 instruction cache misses per 1000 instructions of the big data
applications are higher than in the traditional benchmarks; also, we find that
L3 caches are effective for the big data applications, corroborating the
observation in DCBench.Comment: 12 pages, 6 figures, The 20th IEEE International Symposium On High
Performance Computer Architecture (HPCA-2014), February 15-19, 2014, Orlando,
Florida, US
Best practices for HPM-assisted performance engineering on modern multicore processors
Many tools and libraries employ hardware performance monitoring (HPM) on
modern processors, and using this data for performance assessment and as a
starting point for code optimizations is very popular. However, such data is
only useful if it is interpreted with care, and if the right metrics are chosen
for the right purpose. We demonstrate the sensible use of hardware performance
counters in the context of a structured performance engineering approach for
applications in computational science. Typical performance patterns and their
respective metric signatures are defined, and some of them are illustrated
using case studies. Although these generic concepts do not depend on specific
tools or environments, we restrict ourselves to modern x86-based multicore
processors and use the likwid-perfctr tool under the Linux OS.Comment: 10 pages, 2 figure
Challenging Ubiquitous Inverted Files
Stand-alone ranking systems based on highly optimized inverted file structures are generally considered ātheā solution for building search engines. Observing various developments in software and hardware, we argue however that IR research faces a complex engineering problem in the quest for more flexible yet efficient retrieval systems. We propose to base the development of retrieval systems on āthe database approachā: mapping high-level declarative specifications of the retrieval process into efficient query plans. We present the Mirror DBMS as a prototype implementation of a retrieval system based on this approach
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Preparing sparse solvers for exascale computing.
Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'
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