Advanced service trading for scientific computing over the grid

Hurault, Aurélie; Daydé, Michel; Pantel, Marc

doi:10.1007/s11227-008-0234-9

Cited by 7 publications

(14 citation statements)

References 9 publications

(6 reference statements)

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“…Central to the work we describe in this paper, is the advanced trader we are using [16] and to our knowledge there is no generic approach capable of handling complex expression such as this tool available in a scientific computing environment. Our web portal mainly aims at simplifying the use of scientific libraries deployed as a set of computational services (i.e.…”

Section: Related Workmentioning

confidence: 99%

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On the Easy Use of Scientific Computing Services for Large Scale Linear Algebra and Parallel Decision Making with the P-Grade Portal

et al. 2013

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International audienceScientific research is becoming increasingly dependent on the large-scale analysis of data using distributed computing infrastructures (Grid, cloud, GPU, etc.). Scientific computing (Petitet et al. 1999) aims at constructing mathematical models and numerical solution techniques for solving problems arising in science and engineering. In this paper, we describe the services of an integrated portal based on the P-Grade (Parallel Grid Run-time and Application Development Environment) portal (http://www.p-grade.hu) that enables the solution of large-scale linear systems of equations using direct solvers, makes easier the use of parallel block iterative algorithm and provides an interface for parallel decision making algorithms. The ultimate goal is to develop a single sign on integrated multi-service environment providing an easy access to different kind of mathematical calculations and algorithms to be performed on hybrid distributed computing infrastructures combining the benefits of large clusters, Grid or cloud, when needed

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Section: Related Workmentioning

confidence: 99%

“…The advanced service trading module (based on the work described in [16]) computes the services required to perform the user request i.e. converts the mathematical expressions given by the user into a list of procedures calls (functions of libraries) that answer to its request.…”

Section: Services and High-level User Interfacementioning

confidence: 99%

On the Easy Use of Scientific Computing Services for Large Scale Linear Algebra and Parallel Decision Making with the P-Grade Portal

et al. 2013

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“…The details of the service trader used in this work are described in [HDP09]. For this research, improvements have been made in the way it searches for solutions to the users request.…”

Section: Service Tradingmentioning

confidence: 99%

“…As explained in [HDP09], the trader is based on equational unification and more particularly on the work of Gallier and Snyder [GS89]. It has a type system adapted to overloaded functions with subtyping, based on the λ&−calculus defined by Castagna [CGL92].…”

Section: Inside the Tradermentioning

confidence: 99%

Intelligent Service Trading and Brokering for Distributed Network Services in GridSolve

Hurault

YarKhan

2011

Lecture Notes in Computer Science

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Abstract. One of the great benefits of computational grids is to provide access to a wide range of scientific software and a variety of different computational resources. It is then possible to choose from this large variety of available resources the one that solves a given problem, and even to combine these resources in order to obtain the best solution. Grid service trading (searching for the best combination of software and execution platform according to the user requirements) is thus a crucial issue. Trading relies on the description of available services and computers, on the current state of the grid, and on the user requirements. Given the large amount of services that may be deployed over a Grid, this description cannot be reduced to a simple service name. In this paper, a sophisticated service specification approach similar to algebraic data types is combined with a grid middleware. This leads to a transparent solution for users: they give a mathematical expression to be solved, and the appropriate grid services will be transparently located, composed and executed on their behalf.

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“…This description is used to reason about valid kernel compositions in a view to a full enumeration. We solve the enumeration problem by directly reusing the approach developed in and instead focus our efforts on the selection problem. One possible approach for selection is to develop analytical performance models for each kernel and for their compositions.…”

Section: Introductionmentioning

confidence: 99%

Selecting linear algebra kernel composition using response time prediction

2014

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SUMMARYNumerical linear algebra libraries provide many kernels that can be composed to perform complex computations. For a given computation, there is typically a large number of functionally equivalent kernel compositions. Some of these compositions achieve better response times than others for particular data and when executed on a particular computer architecture. Previous research provides methods to enumerate (a subset of) these kernel compositions. In this work, we study the problem of determining the composition that yields the lowest response time. Our approach is based on a response time prediction for each candidate combination. While this prediction could in principle be obtained using analytical and/or empirical performance models, developing accurate such models is known to be challenging. Instead, we define a feature space that captures salient properties of kernel combinations and predict response time using supervised machine learning. We experiment with a standard set of machine learning algorithms and identify an effective algorithm for our kernel composition selection problem. Using this algorithm, our approach widely outperforms the strategy that would consist in always using the simplest kernel composition and is often close to the fastest kernel compositions among those evaluated. We quantify the potential benefit of our approach if it were to be implemented as part of an interactive computational tool. We find that although the potential benefit is substantial, a limiting factor is the kernel composition enumeration overhead.

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Advanced service trading for scientific computing over the grid

Cited by 7 publications

References 9 publications

On the Easy Use of Scientific Computing Services for Large Scale Linear Algebra and Parallel Decision Making with the P-Grade Portal

On the Easy Use of Scientific Computing Services for Large Scale Linear Algebra and Parallel Decision Making with the P-Grade Portal

Intelligent Service Trading and Brokering for Distributed Network Services in GridSolve

Selecting linear algebra kernel composition using response time prediction

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