In this paper we introduce a methodology for dynamic job reconfiguration driven by the programming model runtime in collaboration with the global resource manager. We improve the system throughput by exploiting malleability ...

Hybrid programming is a promising approach to exploit clusters of multicore systems. Our focus is on the combination of MPI and tasking. This hybrid approach combines the low-latency and high throughput of MPI with the ...

The tasking model of OpenMP 4.0 supports both nesting and the definition of dependences between sibling tasks. A natural way to parallelize many codes with tasks is to first taskify the high-level functions and then to ...

We propose fork-join and task-based hybrid implementations of four classical linear algebra iterative methods (Jacobi, Gauss–Seidel, conjugate gradient and biconjugate gradient stabilized) on CPUs as well as variations of ...

Current web servers are highly multithreaded applications whose scalability benefits from the current multicore/multiprocessor trend. However, some workloads can not capitalize on this because their performance is limited by ...

CUDA and OpenCL are the most widely used programming models to exploit hardware accelerators. Both programming models provide a C-based programming language to write accelerator kernels and a host API used to glue the host ...

State-of-the-art programming approaches generally have a strict division between intra-node shared memory parallelism and inter-node MPI communication. Tasking with dependencies offers a clean, dependable abstraction for ...

Operating systems have historically had to manage only a single type of memory device. The imminent availability of heterogeneous memory devices based on emerging memory technologies confronts the classic single memory ...

Compute and memory access units are two of the most important resources to appropriately manage in current and future multi–/many–core architectures. Memory bandwidth and computational capacity need to be exploited in ...