Christiane Pousa Ribeiro

TL;DR: An user-level interface named Memory Affinity interface (MAi) is described, which allows memory affinity control on Linux based cache-coherent NUMA (ccNUMA) platforms and its main goals are, fine data control, flexibility and portability.

TL;DR: This paper proposes a machine learning-based approach to automatically infer a suitable thread mapping strategy for transactional memory applications and shows that this approach improves performance up to 18.46% compared to the worst case and up to 6.37% over the Linux defaultthread mapping strategy.

TL;DR: NucoLB is introduced, a topology-aware load balancer that focuses on redistributing work while reducing communication costs among and within compute nodes and takes the asymmetric memory access costs present on NUMA multi-core compute nodes, the interconnection network overheads, and the application communication patterns into account in its balancing decisions.

TL;DR: This work aims to provide a method for static mapping for NUMA architectures which does not require any prior knowledge of the application and shows performance gains of up to 75% compared to the native scheduler and memory allocator of the operating system.

TL;DR: This paper introduces Minas, a framework which provides either explicit or automatic memory affinity management with architecture abstraction for ccNUMAs and evaluates the solution on two ccNUMA platforms using two geophysics parallel applications.