Abstract. The emergence and continuing use of multi-core architectures and graphics processing units require changes in the existing software and sometimes even a redesign of the established algorithms in order to take advantage of now prevailing parallelism. Parallel Linear Algebra for Scalable Multi-core Architectures (PLASMA) and Matrix Algebra on GPU and Multics Architectures (MAGMA ) are two projects that aims to achieve high performance and portability across a wide range of multi-core architectures and hybrid systems respectively. We present in this document a comparative study of PLASMA's performance against established linear algebra packages and some preliminary results of MAGMA on hybrid multi-core and GPU systems.

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The emergence and continuing use of multi-core architectures require changes in the existing software and sometimes even a redesign of the established algorithms in order to take advantage of now prevailing parallelism. The Parallel Linear Algebra for Scalable Multi-core Architectures (PLASMA) is a project that aims to achieve both high performance and portability across a wide range of multi-core architectures. We present in this paper a comparative study of PLASMA's performance against established linear algebra packages (LAPACK and ScaLAPACK), against new approaches at parallel execution (Task Based Linear Algebra Subroutines -TBLAS), and against equivalent commercial software offerings (MKL, ESSL and PESSL). Our experiments were conducted on one-sided linear algebra factorizations (LU, QR and Cholesky) and used multi-core architectures (based on Intel Xeon EMT64 and IBM Power6). The performance results show improvements brought by new algorithms on up to 32 cores -the largest multi-core system we could access.

The emergence of accelerators as standard computing resources on supercomputers and the subsequent architectural complexity increase revived the need for high-level parallel programming paradigms. Sequential task-based programming model has been shown to efficiently meet this challenge on a single multicore node possibly enhanced with accelerators, which motivated its support in the OpenMP 4.0 standard. In this paper, we show that this paradigm can also be employed to achieve high performance on modern supercomputers composed of multiple such nodes, with extremely limited changes in the user code. To prove this claim, we have extended the StarPU runtime system with an advanced inter-node data management layer that supports this model by posting communications automatically. We illustrate our discussion with the task-based tile Cholesky algorithm that we implemented on top of this new runtime system layer. We show that it enables very high productivity while achieving a performance competitive with both the pure Message Passing Interface (MPI)-based ScaLAPACK Cholesky reference implementation and the DPLASMA Cholesky code, which implements another (non-sequential) task-based programming paradigm.

International audienceFast Multipole Methods (FMM) are a fundamental operation for the simulation of many physical problems. The high performance design of such methods usually requires to carefully tune the algorithm for both the targeted physics and hardware. In this paper, we propose a new approach that achieves high performance across architectures. Our method consists of expressing the FMM algorithm as a task flow and employing a state- of-the-art runtime system, StarPU, to process the tasks on the different computing units. We carefully design the task flow, the mathematical operators, their implementations as well as scheduling schemes. Potentials and forces on 200 million particles are computed in 42.3 seconds on a homogeneous 160 cores SGI Altix UV 100 and good scalability is shown