44 research outputs found
Batch Size Influence on Performance of Graphic and Tensor Processing Units during Training and Inference Phases
The impact of the maximally possible batch size (for the better runtime) on
performance of graphic processing units (GPU) and tensor processing units (TPU)
during training and inference phases is investigated. The numerous runs of the
selected deep neural network (DNN) were performed on the standard MNIST and
Fashion-MNIST datasets. The significant speedup was obtained even for extremely
low-scale usage of Google TPUv2 units (8 cores only) in comparison to the quite
powerful GPU NVIDIA Tesla K80 card with the speedup up to 10x for training
stage (without taking into account the overheads) and speedup up to 2x for
prediction stage (with and without taking into account overheads). The precise
speedup values depend on the utilization level of TPUv2 units and increase with
the increase of the data volume under processing, but for the datasets used in
this work (MNIST and Fashion-MNIST with images of sizes 28x28) the speedup was
observed for batch sizes >512 images for training phase and >40 000 images for
prediction phase. It should be noted that these results were obtained without
detriment to the prediction accuracy and loss that were equal for both GPU and
TPU runs up to the 3rd significant digit for MNIST dataset, and up to the 2nd
significant digit for Fashion-MNIST dataset.Comment: 10 pages, 7 figures, 2 table
From Quantity to Quality: Massive Molecular Dynamics Simulation of Nanostructures under Plastic Deformation in Desktop and Service Grid Distributed Computing Infrastructure
The distributed computing infrastructure (DCI) on the basis of BOINC and
EDGeS-bridge technologies for high-performance distributed computing is used
for porting the sequential molecular dynamics (MD) application to its parallel
version for DCI with Desktop Grids (DGs) and Service Grids (SGs). The actual
metrics of the working DG-SG DCI were measured, and the normal distribution of
host performances, and signs of log-normal distributions of other
characteristics (CPUs, RAM, and HDD per host) were found. The practical
feasibility and high efficiency of the MD simulations on the basis of DG-SG DCI
were demonstrated during the experiment with the massive MD simulations for the
large quantity of aluminum nanocrystals (-). Statistical
analysis (Kolmogorov-Smirnov test, moment analysis, and bootstrapping analysis)
of the defect density distribution over the ensemble of nanocrystals had shown
that change of plastic deformation mode is followed by the qualitative change
of defect density distribution type over ensemble of nanocrystals. Some
limitations (fluctuating performance, unpredictable availability of resources,
etc.) of the typical DG-SG DCI were outlined, and some advantages (high
efficiency, high speedup, and low cost) were demonstrated. Deploying on DG DCI
allows to get new scientific from the simulated
of numerous configurations by harnessing sufficient computational power to
undertake MD simulations in a wider range of physical parameters
(configurations) in a much shorter timeframe.Comment: 13 pages, 11 pages (http://journals.agh.edu.pl/csci/article/view/106
Performance Evaluation of Distributed Computing Environments with Hadoop and Spark Frameworks
Recently, due to rapid development of information and communication
technologies, the data are created and consumed in the avalanche way.
Distributed computing create preconditions for analyzing and processing such
Big Data by distributing the computations among a number of compute nodes. In
this work, performance of distributed computing environments on the basis of
Hadoop and Spark frameworks is estimated for real and virtual versions of
clusters. As a test task, we chose the classic use case of word counting in
texts of various sizes. It was found that the running times grow very fast with
the dataset size and faster than a power function even. As to the real and
virtual versions of cluster implementations, this tendency is the similar for
both Hadoop and Spark frameworks. Moreover, speedup values decrease
significantly with the growth of dataset size, especially for virtual version
of cluster configuration. The problem of growing data generated by IoT and
multimodal (visual, sound, tactile, neuro and brain-computing, muscle and eye
tracking, etc.) interaction channels is presented. In the context of this
problem, the current observations as to the running times and speedup on Hadoop
and Spark frameworks in real and virtual cluster configurations can be very
useful for the proper scaling-up and efficient job management, especially for
machine learning and Deep Learning applications, where Big Data are widely
present.Comment: 5 pages, 1 table, 2017 IEEE International Young Scientists Forum on
Applied Physics and Engineering (YSF-2017) (Lviv, Ukraine
Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes
The basic features of some of the most versatile and popular open source
frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are
considered and compared. Their comparative analysis was performed and
conclusions were made as to the advantages and disadvantages of these
platforms. The performance tests for the de facto standard MNIST data set were
carried out on H2O framework for deep learning algorithms designed for CPU and
GPU platforms for single-threaded and multithreaded modes of operation Also, we
present the results of testing neural networks architectures on H2O platform
for various activation functions, stopping metrics, and other parameters of
machine learning algorithm. It was demonstrated for the use case of MNIST
database of handwritten digits in single-threaded mode that blind selection of
these parameters can hugely increase (by 2-3 orders) the runtime without the
significant increase of precision. This result can have crucial influence for
optimization of available and new machine learning methods, especially for
image recognition problems.Comment: 15 pages, 11 figures, 4 tables; this paper summarizes the activities
which were started recently and described shortly in the previous conference
presentations arXiv:1706.02248 and arXiv:1707.04940; it is accepted for
Springer book series "Advances in Intelligent Systems and Computing