4,430 research outputs found
TensorFlow Enabled Genetic Programming
Genetic Programming, a kind of evolutionary computation and machine learning
algorithm, is shown to benefit significantly from the application of vectorized
data and the TensorFlow numerical computation library on both CPU and GPU
architectures. The open source, Python Karoo GP is employed for a series of 190
tests across 6 platforms, with real-world datasets ranging from 18 to 5.5M data
points. This body of tests demonstrates that datasets measured in tens and
hundreds of data points see 2-15x improvement when moving from the scalar/SymPy
configuration to the vector/TensorFlow configuration, with a single core
performing on par or better than multiple CPU cores and GPUs. A dataset
composed of 90,000 data points demonstrates a single vector/TensorFlow CPU core
performing 875x better than 40 scalar/Sympy CPU cores. And a dataset containing
5.5M data points sees GPU configurations out-performing CPU configurations on
average by 1.3x.Comment: 8 pages, 5 figures; presented at GECCO 2017, Berlin, German
Correlated Resource Models of Internet End Hosts
Understanding and modelling resources of Internet end hosts is essential for
the design of desktop software and Internet-distributed applications. In this
paper we develop a correlated resource model of Internet end hosts based on
real trace data taken from the SETI@home project. This data covers a 5-year
period with statistics for 2.7 million hosts. The resource model is based on
statistical analysis of host computational power, memory, and storage as well
as how these resources change over time and the correlations between them. We
find that resources with few discrete values (core count, memory) are well
modeled by exponential laws governing the change of relative resource
quantities over time. Resources with a continuous range of values are well
modeled with either correlated normal distributions (processor speed for
integer operations and floating point operations) or log-normal distributions
(available disk space). We validate and show the utility of the models by
applying them to a resource allocation problem for Internet-distributed
applications, and demonstrate their value over other models. We also make our
trace data and tool for automatically generating realistic Internet end hosts
publicly available
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Computational Strategies for Scalable Genomics Analysis.
The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications
GPUs as Storage System Accelerators
Massively multicore processors, such as Graphics Processing Units (GPUs),
provide, at a comparable price, a one order of magnitude higher peak
performance than traditional CPUs. This drop in the cost of computation, as any
order-of-magnitude drop in the cost per unit of performance for a class of
system components, triggers the opportunity to redesign systems and to explore
new ways to engineer them to recalibrate the cost-to-performance relation. This
project explores the feasibility of harnessing GPUs' computational power to
improve the performance, reliability, or security of distributed storage
systems. In this context, we present the design of a storage system prototype
that uses GPU offloading to accelerate a number of computationally intensive
primitives based on hashing, and introduce techniques to efficiently leverage
the processing power of GPUs. We evaluate the performance of this prototype
under two configurations: as a content addressable storage system that
facilitates online similarity detection between successive versions of the same
file and as a traditional system that uses hashing to preserve data integrity.
Further, we evaluate the impact of offloading to the GPU on competing
applications' performance. Our results show that this technique can bring
tangible performance gains without negatively impacting the performance of
concurrently running applications.Comment: IEEE Transactions on Parallel and Distributed Systems, 201
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