25 research outputs found
Grain - A Java Analysis Framework for Total Data Readout
Grain is a data analysis framework developed to be used with the novel Total
Data Readout data acquisition system. In Total Data Readout all the electronics
channels are read out asynchronously in singles mode and each data item is
timestamped. Event building and analysis has to be done entirely in the
software post-processing the data stream. A flexible and efficient event parser
and the accompanying software framework have been written entirely in Java. The
design and implementation of the software are discussed along with experiences
gained in running real-life experiments.Comment: Submitted to NIM
Performance Analysis of BigDecimal Arithmetic Operation in Java
The Java programming language provides binary floating-point primitive data types such as float and double to represent decimal numbers. However, these data types cannot represent decimal numbers with complete accuracy, which may cause precision errors while performing calculations. To achieve better precision, Java provides the BigDecimal class. Unlike float and double, which use approximation, this class is able to represent the exact value of a decimal number. However, it comes with a drawback: BigDecimal is treated as an object and requires additional CPU and memory usage to operate with. In this paper, statistical data are presented of performance impact on using BigDecimal compared to the double data type. As test cases, common mathematical processes were used, such as calculating mean value, sorting, and multiplying matrices
Derivative-free optimization and filter methods to solve nonlinear constrained problems
In real optimization problems, usually the analytical expression of the objective function is not known, nor
its derivatives, or they are complex. In these cases it becomes essential to use optimization methods where
the calculation of the derivatives, or the verification of their existence, is not necessary: the Direct Search
Methods or Derivative-free Methods are one solution.
When the problem has constraints, penalty functions are often used. Unfortunately the choice of the
penalty parameters is, frequently, very difficult, because most strategies for choosing it are heuristics
strategies. As an alternative to penalty function appeared the filter methods. A filter algorithm introduces
a function that aggregates the constrained violations and constructs a biobjective problem. In this problem
the step is accepted if it either reduces the objective function or the constrained violation. This implies that
the filter methods are less parameter dependent than a penalty function.
In this work, we present a new direct search method, based on simplex methods, for general constrained
optimization that combines the features of the simplex method and filter methods. This method does not
compute or approximate any derivatives, penalty constants or Lagrange multipliers. The basic idea of
simplex filter algorithm is to construct an initial simplex and use the simplex to drive the search. We
illustrate the behavior of our algorithm through some examples. The proposed methods were implemented
in Java
New tracks for future computational platforms for engineering applications
The purpose of this paper is to address new tracks for the future generation ofcomputational applications in mechanics and related branches. We advocate thatmodern computational tools will have to deal with complex strongly coupled multi-physics multi-scale problems. Moreover, heterogeneous distributed multi-processorssystems are used today for the numerical simulations. We pose here some basicideas for the design of modern computational applications. All the illustrations arebased on finite elements strategies implemented in a pure Java paradigm
Adaptive Scheduling Across a Distributed Computation Platform
A programmable Java distributed system, which
adapts to available resources, has been developed to minimise the
overall processing time of computationally intensive problems.
The system exploits the free resources of a heterogeneous set of computers
linked together by a network, communicating using
SUN Microsystems' Remote Method Invocation and Java sockets.
It uses a multi-tiered distributed system model, which in principal allows for a system of unbounded size.
The system consists of an n-ary tree of
nodes where the internal nodes perform the scheduling and the
leaves do the processing. The scheduler nodes communicate in a
peer-to-peer manner and the processing nodes operate in a strictly
client-server manner with their respective scheduler. The
independent schedulers on each tier of the tree dynamically allocate resources
between problems based on the constantly changing characteristics
of the underlying network. The system has been evaluated over a network of 86
PCs with a bioinformatics application and the travelling salesman
optimisation problem
Adaptive sampling-based profiling techniques for optimizing the distributed JVM runtime
Extending the standard Java virtual machine (JVM) for cluster-awareness is a transparent approach to scaling out multithreaded Java applications. While this clustering solution is gaining momentum in recent years, efficient runtime support for fine-grained object sharing over the distributed JVM remains a challenge. The system efficiency is strongly connected to the global object sharing profile that determines the overall communication cost. Once the sharing or correlation between threads is known, access locality can be optimized by collocating highly correlated threads via dynamic thread migrations. Although correlation tracking techniques have been studied in some page-based sof Tware DSM systems, they would entail prohibitively high overheads and low accuracy when ported to fine-grained object-based systems. In this paper, we propose a lightweight sampling-based profiling technique for tracking inter-thread sharing. To preserve locality across migrations, we also propose a stack sampling mechanism for profiling the set of objects which are tightly coupled with a migrant thread. Sampling rates in both techniques can vary adaptively to strike a balance between preciseness and overhead. Such adaptive techniques are particularly useful for applications whose sharing patterns could change dynamically. The profiling results can be exploited for effective thread-to-core placement and dynamic load balancing in a distributed object sharing environment. We present the design and preliminary performance result of our distributed JVM with the profiling implemented. Experimental results show that the profiling is able to obtain over 95% accurate global sharing profiles at a cost of only a few percents of execution time increase for fine- to medium- grained applications. © 2010 IEEE.published_or_final_versionThe 24th IEEE International Symposium on Parallel & Distributed Processing (IPDPS 2010), Atlanta, GA., 19-23 April 2010. In Proceedings of the 24th IPDPS, 2010, p. 1-1
Non-averaged regularized formulations as an alternative to semi-analytical orbit propagation methods
This paper is concerned with the comparison of semi-analytical and
non-averaged propagation methods for Earth satellite orbits. We analyse the
total integration error for semi-analytical methods and propose a novel
decomposition into dynamical, model truncation, short-periodic, and numerical
error components. The first three are attributable to distinct approximations
required by the method of averaging, which fundamentally limit the attainable
accuracy. In contrast, numerical error, the only component present in
non-averaged methods, can be significantly mitigated by employing adaptive
numerical algorithms and regularized formulations of the equations of motion.
We present a collection of non-averaged methods based on the integration of
existing regularized formulations of the equations of motion through an
adaptive solver. We implemented the collection in the orbit propagation code
THALASSA, which we make publicly available, and we compared the non-averaged
methods to the semi-analytical method implemented in the orbit propagation tool
STELA through numerical tests involving long-term propagations (on the order of
decades) of LEO, GTO, and high-altitude HEO orbits. For the test cases
considered, regularized non-averaged methods were found to be up to two times
slower than semi-analytical for the LEO orbit, to have comparable speed for the
GTO, and to be ten times as fast for the HEO (for the same accuracy). We show
for the first time that efficient implementations of non-averaged regularized
formulations of the equations of motion, and especially of non-singular element
methods, are attractive candidates for the long-term study of high-altitude and
highly elliptical Earth satellite orbits.Comment: 33 pages, 10 figures, 7 tables. Part of the CMDA Topical Collection
on "50 years of Celestial Mechanics and Dynamical Astronomy". Comments and
feedback are encourage