Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Beyond Reuse Distance Analysis: Dynamic Analysis for Characterization of
  Data Locality Potential   Emerging computer architectures will feature drastically decreased flops/byte
(ratio of peak processing rate to memory bandwidth) as highlighted by recent
studies on Exascale architectural trends. Further, flops are getting cheaper
while the energy cost of data movement is increasingly dominant. The
understanding and characterization of data locality properties of computations
is critical in order to guide efforts to enhance data locality. Reuse distance
analysis of memory address traces is a valuable tool to perform data locality
characterization of programs. A single reuse distance analysis can be used to
estimate the number of cache misses in a fully associative LRU cache of any
size, thereby providing estimates on the minimum bandwidth requirements at
different levels of the memory hierarchy to avoid being bandwidth bound.
However, such an analysis only holds for the particular execution order that
produced the trace. It cannot estimate potential improvement in data locality
through dependence preserving transformations that change the execution
schedule of the operations in the computation. In this article, we develop a
novel dynamic analysis approach to characterize the inherent locality
properties of a computation and thereby assess the potential for data locality
enhancement via dependence preserving transformations. The execution trace of a
code is analyzed to extract a computational directed acyclic graph (CDAG) of
the data dependences. The CDAG is then partitioned into convex subsets, and the
convex partitioning is used to reorder the operations in the execution trace to
enhance data locality. The approach enables us to go beyond reuse distance
analysis of a single specific order of execution of the operations of a
computation in characterization of its data locality properties. It can serve a
valuable role in identifying promising code regions for manual transformation,
as well as assessing the effectiveness of compiler transformations for data
locality enhancement. We demonstrate the effectiveness of the approach using a
number of benchmarks, including case studies where the potential shown by the
analysis is exploited to achieve lower data movement costs and better
performance.

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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: q-fin
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The categories of this paper are: physics
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
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The categories of this paper are: nlin
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The categories of this paper are: comp-gas
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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org