9,116 research outputs found
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
Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case 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.Comment: 4 pages, 6 figures, 4 tables; XIIth International Scientific and
Technical Conference on Computer Sciences and Information Technologies (CSIT
2017), Lviv, Ukrain
Teaching Concurrent Software Design: A Case Study Using Android
In this article, we explore various parallel and distributed computing topics
from a user-centric software engineering perspective. Specifically, in the
context of mobile application development, we study the basic building blocks
of interactive applications in the form of events, timers, and asynchronous
activities, along with related software modeling, architecture, and design
topics.Comment: Submitted to CDER NSF/IEEE-TCPP Curriculum Initiative on Parallel and
Distributed Computing - Core Topics for Undergraduate
Answer-set programming as a new approach to event-sequence testing
In many applications, faults are triggered by events that occur in a particular order. Based on the assumption that most bugs are caused by the interaction of a low number of events, Kuhn et al. recently introduced sequence covering arrays (SCAs) as suitable designs for event sequence testing. In practice, directly applying SCAs for testing is often impaired by additional constraints, and SCAs have to be adapted to fit application-specific needs. Modifying precomputed SCAs to account for problem variations can be problematic, if not impossible, and developing dedicated algorithms is costly. In this paper, we propose answer-set programming (ASP), a well-known knowledge-representation formalism from the area of artificial intelligence based on logic programming, as a declarative paradigm for computing SCAs. Our approach allows to concisely state complex coverage criteria in an elaboration tolerant way, i.e., small variations of a problem specification require only small modifications of the ASP representation
Introduction to the 28th International Conference on Logic Programming Special Issue
We are proud to introduce this special issue of the Journal of Theory and
Practice of Logic Programming (TPLP), dedicated to the full papers accepted for
the 28th International Conference on Logic Programming (ICLP). The ICLP
meetings started in Marseille in 1982 and since then constitute the main venue
for presenting and discussing work in the area of logic programming
Threads and Or-Parallelism Unified
One of the main advantages of Logic Programming (LP) is that it provides an
excellent framework for the parallel execution of programs. In this work we
investigate novel techniques to efficiently exploit parallelism from real-world
applications in low cost multi-core architectures. To achieve these goals, we
revive and redesign the YapOr system to exploit or-parallelism based on a
multi-threaded implementation. Our new approach takes full advantage of the
state-of-the-art fast and optimized YAP Prolog engine and shares the underlying
execution environment, scheduler and most of the data structures used to
support YapOr's model. Initial experiments with our new approach consistently
achieve almost linear speedups for most of the applications, proving itself as
a good alternative for exploiting implicit parallelism in the currently
available low cost multi-core architectures.Comment: 17 pages, 21 figures, International Conference on Logic Programming
(ICLP 2010
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