Parallax : an implementation of ELGDF (Extended Large Grain Data Flow)

The major obstacle to widespread parallel programming of multiprocessors is the lack of a convenient parallel programming system. PPSE (Parallel Programming Support Environment) is a unified approach to parallel programming. Parallax is developed as a component of PPSE based on ELGDF which is a graphical language for designing parallel programs. The goals of Parallax are to solve the following problems: 1) How to represent parallelism naturally in an application, 2) How to make a parallel program portable across different parallel computers, 3) How to remove time-dependent problems from the programmer's concern, 4) How to provide a standard software description format that can be used by other tools such as automated schedulers and performance analyzers, and 5) How to increase programmer's productivity. Our approach to these problems are: 1) ELGDF notation: Parallax uses ELGDF notation that allows a wide representation of a variety of parallel programs in a natural way for both shared-memory and message-passing models using higher level parallel abstractions. Program details such as synchronization are handled by the system using reusable libraries specific to each target system. Parallax is a CASE tool which supports common Software­Engineering techniques such as hierarchical design concepts that support both top-down and bottom-up design using visual­-programming techniques. 2) PP Design File: Parallel program designs are stored in a standard format in a PP (Parallel Program) Design File. This representation can be transformed into a task graph representation at different levels of granularity. The task graph of the problem can be used to estimate execution time and performance of the resulting parallel program. Parallax is implemented on Macintosh as part of the PPSE project. It has been used to represent the design for both shared and distributed memory machines, with individual programs written in FORTRAN and C, with and without Linda support, and it has successfully produced program designs which can be analyzed by other tools such as TaskGrapher[5] and SuperGlue[6]. Parallax currently does not automatically produce a task graph, nor does it fully represent programs written in high level languages such as FORTRAN. Finally, ELGDF currently lacks formal semantics for representing computations

Metaheuristics “In the Large”

Many people have generously given their time to the various activities of the MitL initiative. Particular gratitude is due to Adam Barwell, John A. Clark, Patrick De Causmaecker, Emma Hart, Zoltan A. Kocsis, Ben Kovitz, Krzysztof Krawiec, John McCall, Nelishia Pillay, Kevin Sim, Jim Smith, Thomas Stutzle, Eric Taillard and Stefan Wagner. J. Swan acknowledges the support of UK EPSRC grant EP/J017515/1 and the EU H2020 SAFIRE Factories project. P. GarciaSanchez and J. J. Merelo acknowledges the support of TIN201785727-C4-2-P by the Spanish Ministry of Economy and Competitiveness. M. Wagner acknowledges the support of the Australian Research Council grants DE160100850 and DP200102364.Following decades of sustained improvement, metaheuristics are one of the great success stories of opti- mization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to sup- port the development, analysis and comparison of new approaches. To this end, we present the vision and progress of the Metaheuristics “In the Large”project. The conceptual underpinnings of the project are: truly extensible algorithm templates that support reuse without modification, white box problem descriptions that provide generic support for the injection of domain specific knowledge, and remotely accessible frameworks, components and problems that will enhance reproducibility and accelerate the field’s progress. We argue that, via such principled choice of infrastructure support, the field can pur- sue a higher level of scientific enquiry. We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) EP/J017515/1EU H2020 SAFIRE Factories projectSpanish Ministry of Economy and Competitiveness TIN201785727-C4-2-PAustralian Research Council DE160100850 DP20010236