151,513 research outputs found
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.Comment: Transaction on Architecture and Code Optimization (2014
Splitting from Nonrelativistic Renormalization Group
We compute the hyperfine splitting in a heavy quarkonium composed of
different flavors in next-to-leading logarithmic approximation using the
nonrelativistic renormalization group. We predict the mass difference of the
vector and pseudoscalar charm-bottom mesons to be MeV.Comment: Eq.(22) and Appendix corrected, numerical results slightly changed.
arXiv admin note: text overlap with arXiv:hep-ph/031208
Improving the Effective Potential, Multi-Mass Problem and Modified Mass-Dependent Scheme
We present a new procedure for improving the effective potential by using
renormalization group equation (RGE) in the presence of several mass scales. We
propose a modification of the mass-dependent (MD) renormalization scheme, MDbar
scheme, so that the scalar mass parameter runs at most logarithmically on the
one hand and the decoupling of heavy particles is naturally incorporated in the
RGE's on the other. Thanks to these properties, the procedure in MDbar scheme
turns out to be very simple compared with the regionwise procedure in MSbar
scheme proposed previously. The relation with other schemes is also discussed
both analytically and numerically.Comment: 34 pages, 9 Post-Script figures are include
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