9,121 research outputs found
Cache behavior prediction by abstract interpretation
Abstract interpretation is a technique for the static detection of dynamic properties of programs. It is semantics based, that is, it computes approximative properties of the semantics of programs. On this basis, it allows for correctness proofs of analyses. It replaces commonly used ad hoc techniques by systematic, provable ones, and it allows the automatic generation of analyzers from specifications as in the Program Analyzer Generator, PAG. In this paper, abstract interpretation is applied to the problem of predicting the cache behavior of programs. Abstract semantics of machine programs are defined which determine the contents of caches. For interprocedural analysis, existing methods are examined and a new approach that is especially tailored for the cache analysis is presented. This allows for a static classification of the cache behavior of memory references of programs. The calculated information can be used to sharpen worst case execution time estimations. It is possible to analyze instruction, data, and combined instruction/data caches for common (re)placement and write strategies. Experimental results are presented that demonstrate the applicability of the analysis
Automatic Loop Kernel Analysis and Performance Modeling With Kerncraft
Analytic performance models are essential for understanding the performance
characteristics of loop kernels, which consume a major part of CPU cycles in
computational science. Starting from a validated performance model one can
infer the relevant hardware bottlenecks and promising optimization
opportunities. Unfortunately, analytic performance modeling is often tedious
even for experienced developers since it requires in-depth knowledge about the
hardware and how it interacts with the software. We present the "Kerncraft"
tool, which eases the construction of analytic performance models for streaming
kernels and stencil loop nests. Starting from the loop source code, the problem
size, and a description of the underlying hardware, Kerncraft can ideally
predict the single-core performance and scaling behavior of loops on multicore
processors using the Roofline or the Execution-Cache-Memory (ECM) model. We
describe the operating principles of Kerncraft with its capabilities and
limitations, and we show how it may be used to quickly gain insights by
accelerated analytic modeling.Comment: 11 pages, 4 figures, 8 listing
ShenZhen transportation system (SZTS): a novel big data benchmark suite
Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads, however, are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. In this paper, we propose ShenZhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as HiBench and CloudRank-D, consist of generic algorithms with synthetic inputs. We perform a cross-layer workload characterization at the microarchitecture level, the operating system (OS) level, and the job level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and we propose a methodology for identifying representative input data sets
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