Andreas Wolf

A class of OpenMP applications involving nested parallelism

Today, OpenMP is the de facto standard for portable shared-memory programming supporting multiple levels of parallelism. Unfortunately, most of the current OpenMP implementations are not capable of fully exploiting more than one level of parallelism. With the increasing number of processors available in high-performance computing resources, the number of applications that would benefit from multilevel parallelism is also increasing. Applying automatic differentiation to OpenMP programs is introduced as a new class of OpenMP applications with nested parallelism.

How to Scale Nested OpenMP Applications on the ScaleMP vSMP Architecture

The novel ScaleMP vSMP architecture employs commodity x86-based servers with an InfiniBand network to assemble a large shared memory system at an attractive price point. We examine this combined hardware- and softwareapproach of a DSM system using both system-level kernel benchmarks as well as real-world application codes. We compare this architecture with traditional shared memory machines and elaborate on strategies to tune application codes parallelized with OpenMP on multiple levels. Finally we summarize the necessary conditions which a scalable application has to fulfill in order to profit from the full potential of the ScaleMP approach.

Semi-automatic parallelization of direct and inverse problems for geothermal simulation

We describe a strategy for parallelizing a geothermal simulation package using the shared-memory programming model OpenMP. During the code development OpenMP is employed for the direct problem in such a way that, in a subsequent step, the OpenMP-parallelized code can be transformed via automatic differentiation into an OpenMP-parallelized code capable of computing derivatives for the inverse problem. Performance results on a Sun Fire X4600 using up to 16 threads are reported demonstrating that, for the derivative computation, an approach using nested parallelism is more scalable than a single level of parallelism.