26,753 research outputs found
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Run Time Approximation of Non-blocking Service Rates for Streaming Systems
Stream processing is a compute paradigm that promises safe and efficient
parallelism. Modern big-data problems are often well suited for stream
processing's throughput-oriented nature. Realization of efficient stream
processing requires monitoring and optimization of multiple communications
links. Most techniques to optimize these links use queueing network models or
network flow models, which require some idea of the actual execution rate of
each independent compute kernel within the system. What we want to know is how
fast can each kernel process data independent of other communicating kernels.
This is known as the "service rate" of the kernel within the queueing
literature. Current approaches to divining service rates are static. Modern
workloads, however, are often dynamic. Shared cloud systems also present
applications with highly dynamic execution environments (multiple users,
hardware migration, etc.). It is therefore desirable to continuously re-tune an
application during run time (online) in response to changing conditions. Our
approach enables online service rate monitoring under most conditions,
obviating the need for reliance on steady state predictions for what are
probably non-steady state phenomena. First, some of the difficulties associated
with online service rate determination are examined. Second, the algorithm to
approximate the online non-blocking service rate is described. Lastly, the
algorithm is implemented within the open source RaftLib framework for
validation using a simple microbenchmark as well as two full streaming
applications.Comment: technical repor
TensorFlow Doing HPC
TensorFlow is a popular emerging open-source programming framework supporting
the execution of distributed applications on heterogeneous hardware. While
TensorFlow has been initially designed for developing Machine Learning (ML)
applications, in fact TensorFlow aims at supporting the development of a much
broader range of application kinds that are outside the ML domain and can
possibly include HPC applications. However, very few experiments have been
conducted to evaluate TensorFlow performance when running HPC workloads on
supercomputers. This work addresses this lack by designing four traditional HPC
benchmark applications: STREAM, matrix-matrix multiply, Conjugate Gradient (CG)
solver and Fast Fourier Transform (FFT). We analyze their performance on two
supercomputers with accelerators and evaluate the potential of TensorFlow for
developing HPC applications. Our tests show that TensorFlow can fully take
advantage of high performance networks and accelerators on supercomputers.
Running our TensorFlow STREAM benchmark, we obtain over 50% of theoretical
communication bandwidth on our testing platform. We find an approximately 2x,
1.7x and 1.8x performance improvement when increasing the number of GPUs from
two to four in the matrix-matrix multiply, CG and FFT applications
respectively. All our performance results demonstrate that TensorFlow has high
potential of emerging also as HPC programming framework for heterogeneous
supercomputers.Comment: Accepted for publication at The Ninth International Workshop on
Accelerators and Hybrid Exascale Systems (AsHES'19
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