International audienceThis paper is about using the existing Monte Carlo approach for pricing European and American contracts on a state-of-the-art graphics processing unit (GPU) architecture. First, we adapt on a cluster of GPUs two different suitable paradigms of parallelizing random number generators, which were developed for CPU clusters. Because in financial applications, we request results within seconds of simulation, the sufficiently large computations should be implemented on a cluster of machines. Thus, we make the European contract comparison between CPUs and GPUs using from one up to 16 nodes of a CPU/GPU cluster. We show that using GPUs for European contracts reduces the execution time by ∼ 40 and diminishes the energy consumed by ∼ 50 during the simulation. In the second set of experiments, we investigate the benefits of using GPUs' parallelization for pricing American options that require solving an optimal stopping problem and which we implement using the Longstaff and Schwartz regression method. The speedup result obtained for American options varies between two and 10 according to the number of generated paths, the dimensions, and the time discretization
DACCOSIM is a multi-simulation environment for continuous time systems, relying on FMI standard, making easy the design of a multi-simulation graph, and specially developed for multi-core PC clusters, in order to achieve speedup and size up. However, the distribution of the simulation graph remains complex and is still the responsibility of the simulation developer. This paper introduces DACCOSIM parallel and distributed architecture, and our strategies to achieve efficient multi-simulation graph distribution on multi-core clusters. Some performance experiments on two clusters, running up to 81 simulation components (FMU) and using up to 16 multi-core computing nodes, are shown. Performances measured on our faster cluster exhibit a good scalability, but some limitations of current DACCOSIM implementation are discussed.
International audienceDistributing applications over PC clusters to speed-up or size-up the execution is now commonplace. Yet efficiently tolerating faults of these systems is a major issue. To ease the addition of checkpoint-based fault tolerance at the application level, we introduce a Model for Low-Overhead Tolerance of Faults (MoLOToF) which is based on structuring applications using fault-tolerant skeletons. MoLOToF also encourages collaborations with the programmer and the execution environment. The skeletons are adapted to specific parallelization paradigms and yield what can be called fault-tolerant algorithmic skeletons. The application of MoLOToF to the SPMD parallelization paradigm results in our proposed FT-SPMD framework. Experiments show that the complexity for developing an application is small and the use of the framework has a small impact on performance. Comparisons with existing system-level checkpoint solutions, namely LAM/MPI and DMTCP, point out that FT-SPMD has a lower runtime overhead while being more robust when a higher level of fault tolerance is required
This paper introduces a Grid software architecture offering fault tolerance, dynamic and aggressive load balancing and two complementary parallel programming paradigms. Experiments with financial applications on a real multi-site Grid assess this solution.This architecture has been designed to run industrial and financial applications, that are frequently time constrained and CPU consuming, feature both tightly and loosely coupled parallelism requiring generic programming paradigm, and adopt client-server business architecture.
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