An introduction to ray tracing
A comparison of implicit and explicit parallel programming
Journal of Parallel and Distributed Computing
Parallel programming in OpenMP
Parallel programming in OpenMP
Structure and Interpretation of Computer Programs
Structure and Interpretation of Computer Programs
Using AspectJ to separate concerns in parallel scientific Java code
Proceedings of the 3rd international conference on Aspect-oriented software development
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
JavaSymphony, a programming model for the Grid
Future Generation Computer Systems
Guest Editors' Introduction: Aspect-Oriented Programming
IEEE Software
Computer
A process for separation of crosscutting grid concerns
Proceedings of the 2006 ACM symposium on Applied computing
Concurrency and Computation: Practice & Experience
Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment
Journal of Parallel and Distributed Computing
Python for Scientific Computing
Computing in Science and Engineering
Byte-code scheduling of Java programs with branches for desktop grid
Future Generation Computer Systems
JGRIM: An approach for easy gridification of applications
Future Generation Computer Systems
Parallel Scripting with Python
Computing in Science and Engineering
A survey on approaches to gridification
Software—Practice & Experience
Software—Practice & Experience
Towards supporting multiple virtual private computing environments on computational Grids
Advances in Engineering Software
Future Generation Computer Systems
Nested parallelism for multi-core HPC systems using Java
Journal of Parallel and Distributed Computing
Optimization Techniques for Solving Complex Problems
Optimization Techniques for Solving Complex Problems
Proceedings of the 18th ACM international symposium on High performance distributed computing
Scientific Scripting for the Java Platform with jLab
Computing in Science and Engineering
MapReduce: a flexible data processing tool
Communications of the ACM - Amir Pnueli: Ahead of His Time
Satin: A high-level and efficient grid programming model
ACM Transactions on Programming Languages and Systems (TOPLAS)
The Definitive Guide to Jython: Python for the Java Platform
The Definitive Guide to Jython: Python for the Java Platform
Scalable dynamic Monitoring, Analysis and Tuning Environment for parallel applications
Journal of Parallel and Distributed Computing
Computer Languages, Systems and Structures
Recent Advances in Parallel Virtual Machine and Message Passing Interface: 16th European PVM/MPI Users' Group Meeting, Espoo, Finland, September 7-10, ... / Programming and Software Engineering
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
Combining Grid and Cloud Resources by Use of Middleware for SPMD Applications
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
Python: An Ecosystem for Scientific Computing
Computing in Science and Engineering
Advances in Engineering Software
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Grid Computing allows scientists and engineers to run compute intensive experiments that were unfeasible not so long ago. On the downside, for users not proficient in distributed technologies, programming for Grids is difficult, tedious, time-consuming and error-prone. Then, disciplinary users typically waste precious time that could be instead invested into analyzing results. In a previous paper, we introduced BYG (Mateos et al., 2011) [28], a Java-based software that automatically parallelizes sequential applications by directly modifying their compiled codes. In addition, BYG is designed to harness Grid resources by reusing existing Grid platforms and schedulers. In its current shape, however, BYG lacks support for some state-of-the-art Grid schedulers and mechanisms for introducing application-dependent optimizations to parallelized codes. In this paper, we present several extensions to BYG aimed at overcoming these problems and thus improving its applicability and delivered efficiency. We also report experiments by using traditional computational kernels and real-life applications to show the positive practical implications of the proposed extensions.