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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2016
عنوان انگلیسی مقاله:
A Comparative Survey of the HPC and Big Data Paradigms: Analysis and Experiments
ترجمه فارسی عنوان مقاله:
بررسی مقایسه ای پارادایم های HPC و داده های بزرگ: تجزیه و تحلیل و آزمایشات
منبع:
IEEE - 2016 IEEE International Conference on Cluster Computing
نویسنده:
HamidReza Asaadi, Dounia Khaldi and Barbara Chapman
چکیده انگلیسی:
Many scientific data analytic applications need huge
amounts of input, which can often consist of more than several
TBs of data. This emphasizes the high I/O and processing/computational cost requirements of these algorithms. Tasks in these
programs can induce more I/O operations than computations or
the opposite. Hardware also includes nodes with large storage
devices and/or nodes with sophisticated computational capabilities. To embrace the heterogeneity of the hardware systems in
non-cloud and cloud environments, the issues of resource and
job allocation in these environments need to be revisited. HighPerformance Computing (HPC) models, under the leadership
of MPI (plus OpenMP) parallel APIs, have mostly met users’
requirements in terms of high computational performance, while
Big Data frameworks such as Spark have performed likewise in
terms of high-level programming, resiliency and I/O handling.
Therefore, in order to meet the specialized needs of scientists,
there is a need for convergence between HPC and Big Data
ecosystems.
This paper presents a data-supported, comparative survey
of the main current HPC and Big Data programming interfaces, namely MPI, OpenMP, PGAS (OpenSHMEM), Spark,
and Hadoop, and their software stacks. A comprehensive experimental study of these interfaces on a set of benchmarks,
namely reduction and I/O microbenchmarks, the StackExchange
AnswersCount benchmark, and PageRank Benchmark has been
performed on a single platform in order to achieve a fair
comparison. These experiments lead to a thorough discussion
about whether the envisioned convergence is indeed needed or
not, efficient or not, and in particular whether it is the best
solution to tackle future computational challenges.
Keywords: Sparks | Big data | Programming | Electronics packaging | Data models |
Software | Parallel processing
قیمت: رایگان
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