On the effective parallel programming of multi-core processors

Multi-core processors are considered now the only feasible alternative to the large single-core processors which have become limited by technological aspects such as power consumption and heat dissipation. However, due to their inherent parallel structure and their diversity, multi-cores are difficult to program. There is a variety of different approaches to simplify multi-core programming, but most of them are only solving parts of the problem, leaving the rest as (unrealistic) assumptions. This thesis proposes a unitary framework (called MAP) for effective programming of multi-core processors, filling a gap in the multi-core programming models landscape. The framework is designed to assist the programmer in application design, implementation, optimization, and performance analysis. MAP is built using the expertise and guidelines gathered while programming three types of multi-core processors for three different classes of applications. Thus, MAP has several stages: application design, modeling, prototyping, and tuning, as well as performance checkpoints and a performance guided feedback loop. Overall, MAP is a viable application-centric approach to programming for multi-core processors. However, part of the tool support is lacking and more has to be done, as future work, to replace some of the phases which are now developed by hand with (semi-)automated tools.Software TechnologyElectrical Engineering, Mathematics and Computer Scienc

Optimizing a calibration software for radio astronomy

With the turn to multicore in chip design and manufacturing, both consumer and high performance applications can benefit from ubiquitous hardware parallelism. However, the performance improvement to be achieved is not always in the orders of magnitude range. In this paper, we present the challenging example of designing a parallel version of a model fitting algorithm used in calibrating telescope observation data in radio astronomy. The complexity of the application, together with the limited opportunities for code modification, bound the performance gain that any parallel system could achieve. However, we show how classical "bound-and-bottleneck" analysis and optimization using multicore architectures help achieving up to 2.3x "wall clock" speedup compared to the original sequential implementation. We further discuss the reasons for this limitation, and suggest possible solutions to address it

Radio Astronomy Beam Forming on Many-Core Architectures

Abstract—Traditional radio telescopes use large steel dishes to observe radio sources. The largest radio telescope in the world, LOFAR, uses tens of thousands of fixed, omni-directional antennas instead, a novel design that promises ground-breaking research in astronomy. Where traditional tele-scopes use custom-built hardware, LOFAR uses software to do signal processing in real time. This leads to an instrument that is inherently more flexible. However, the enormous data rates and processing requirements (tens to hundreds of teraflops) make this extremely challenging. The next-generation telescope, the SKA, will require exaflops. Unlike traditional instruments, LOFAR and SKA can observe in hundreds of directions simultaneously, using beam forming. This is useful, for example, to search the sky for pulsars (i.e. rapidly rotating highly magnetized neutron stars). Beam forming is an importan