Fast American Basket Option Pricing on a multi-GPU Cluster

8 pagesInternational audienceThis article presents a multi-GPU adaptation of a specific Monte Carlo and classification based method for pricing American basket options, due to Picazo. The first part relates how to combine fine and coarse-grained parallelization to price American basket options. A dynamic strategy of kernel calibration is proposed. Doing so, our implementation on a reasonable size (18) GPU cluster achieves the pricing of a high dimensional (40) option in less than one hour against almost 8 as observed for runs we conducted in the past, using a 64-core cluster (composed of quad-core AMD Opteron 2356). In order to benefit from different GPU device types, we detail the dynamic strategy we have used to load balance GPU calculus which greatly improves the overall pricing time we obtained. An analysis of possible bottleneck effects demonstrates that there is a sequential bottleneck due to the training phase that relies upon the AdaBoost classification method, which prevents the implementation to be fully scalable, and so prevents to envision further decreasing pricing time down to handful of minutes. For this we propose to consider using Random Forests classification method: it is naturally dividable over a cluster, and available like AdaBoost as a black box from the popular Weka machine learning library. However our experimental tests will show that its use is costly

Distributed Snapshot algorithm for multi-active object-based applications

International audienceThis paper exposes an adaptation of the classic algorithm for consistent snapshot in distributed systems with asynchronous processes due to Chandy&Lamport. A snapshot in this context is described as the consistent set of states of all involved communicating processes that allows recovering the whole system after a crash. The reconstructed system state is consistent, even if messages injected into the system from the outside while the snapshot was ongoing may have been lost (if such messages can not be replayed). We expose how to adapt this algorithm to a particular distributed programming model, the Active Object model (in its multi-active version). We applied it successfully to a non trivial distributed application programmed using Active Objects serving as a publish/subscribe and storage of events middleware, dubbed the EventCloud

Programming distributed and adaptable autonomous components--the GCM/ProActive framework

International audienceComponent-oriented software has become a useful tool to build larger and more complex systems by describing the application in terms of encapsulated, loosely coupled entities called components. At the same time, asynchronous programming patterns allow for the development of efficient distributed applications. While several component models and frameworks have been proposed, most of them tightly integrate the component model with the middleware they run upon. This intertwining is generally implicit and not discussed, leading to entangled, hard to maintain code. This article describes our efforts in the development of the GCM/ProActive framework for providing distributed and adaptable autonomous components. GCM/ProActive integrates a component model designed for execution on large-scale environments, with a programming model based on active objects allowing a high degree of distribution and concurrency. This new integrated model provides a more powerful development, composition, and execution environment than other distributed component frameworks. We illustrate that GCM/ProActive is particularly adapted to the programming of autonomic component systems, and to the integration into a service-oriented environment

Towards Grid Monitoring and deployment in Jade, using ProActive

This document describes our current effort to gridify Jade, a java-based environment for the autonomic management of clustered J2EE application servers, developed in the INRIA SARDES research team. Towards this objective, we use the java ProActive grid technology. We first present some of the challenges to turn such an autonomic management system initially dedicated to distributed applications running on clusters of machines, into one that can provide self-management capabilities to large-scale systems, i.e. deployed on grid infrastructures. This leads us to a brief state of the art on grid monitoring systems. Then, we recall the architecture of Jade, and consequently propose to reorganize it in a potentially more scalable way. Practical experiments pertain to the use of the grid deployment feature offered by ProActive to easily conduct the deployment of the Jade system or its revised version on any sort of grid

Towards a flexible data stream analytics platform based on the GCM autonomous software component technology

International audienceBig data stream analytics platforms not only need to support performance-dictated elasticity benefiting for instance from Cloud environments. They should also support analytics that can evolve dynamically from the application viewpoint, given data nature can change so the necessary treatments on them. The benefit is that this can avoid to undeploy the current analytics, modify it off-line, redeploy the new version, and resume the analysis, missing data that arrived in the meantime. We also believe that such evolution should better be driven by autonomic behaviors whenever possible. We argue that a software component based technology, as the one we have developed so far, GCM/ProActive, can be a good fit to these needs. Using it, we present our solution, still under development, named GCM-streaming, which to our knowledge seems to be quite original

A Generic API for Load Balancing in Structured P2P Systems

International audienceReal world datasets are known to be highly skewed, often leading to an important load imbalance issue for distributed systems managing them. To address this issue, there exist almost as many load balancing strategies as there are different systems. When designing a scalable distributed system geared towards handling large amounts of information, it is often not so easy to anticipate which kind of strategy will be the most efficient to maintain adequate performance regarding response time, scalability and reliability at any time. Based on this observation, we describe the methodology behind the building of a generic API to implement and experiment any strategy independently from the rest of the code, prior to a definitive choice for instance. We then show how this API is compatible with famous existing systems and their load balancing scheme. We also present results from our own distributed system which targets the continuous storage of events structured according to the Semantic Web standards, further retrieved by interested parties. As such, our system constitutes a typical example of a Big Data environment

Towards Parallel and Distributed Computing on GPU for American Basket Option Pricing

International audienceThis article presents a GPU adaptation of a specific Monte Carlo and classification based method for pricing American basket options, due to Picazo. Some optimizations are exposed to get good performance of our parallel algorithm on GPU. In order to benefit from different GPU devices, a dynamic strategy of kernel calibration is proposed. Future work is geared towards the use of distributed computing infrastructures such as Grids and Clouds, equipped with GPUs, in order to benefit for even more parallelism in solving such computing intensive problem in mathematical finance

Modular P2P-Based Approach for RDF Data Storage and Retrieval

International audienceOne of the key elements of the Semantic Web is the Resource Description Framework (RDF). Efficient storage and retrieval of RDF data in large scale settings is still challenging and existing solutions are monolithic and thus not very flexible from a software engineering point of view. In this paper, we propose a modular system, based on the scalable Content-Addressable Network (CAN), which gives the possibility to store and retrieve RDF data in large scale settings. We identified and isolated key components forming such system in our design architecture. We have evaluated our system using the Grid'5000 testbed over 300 peers on 75 machines and the outcome of these micro-benchmarks show interesting results in terms of scalability and concurrent queries

Componentising a scientific application for the grid

CoreGRID is a Network of Excellence funded by the European Commission under the Sixth Framework Programm

Évaluation d'une architecture de stockage RDF distribuée

Atelier "Ontologies et Jeux de Données pour évaluer le web sémantique (OJD)"Stocker des informations du web sémantique implique d'être capable de pouvoir potentiellement gérer de très importants volumes de données. D'où le besoin d'opter pour une solution forcément distribuée, entre autres de type pair-à-pair, pour pouvoir passer à l'échelle. Un système de stockage RDF réparti requiert de mettre en place un algorithme particulier pour la résolution des requêtes SPARQL. Les données étant distribuées à travers un réseau de pairs, il est nécessaire d'exécuter une partie de la requête sur certains de ces pairs, puis d'agréger et d'appliquer d'éventuelles conditions de filtrage sur les différents résultats intermédiaires obtenus, avant de pouvoir retourner les résultats finaux. Il existe actuellement une douzaine de benchmarks pour le RDF, mais aucun d'entre eux ne se présente comme étant pensé pour s'adapter à une architecture de stockage réparti. Dans cet article, nous mettons en évidence le nombre de résultats intermédiaires générés par les requêtes SPARQL, un aspect important dans un contexte distribué, et qui nous semble à l'heure actuelle négligé par les benchmarks pour le RDF