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 important technique in signal processing: it is also used in WIFI and 4G cellular networks, radar systems, and health-care microwave imaging instruments. We propose the use of many-core architectures, such as 48-core CPU systems and Graphics Processing Units (GPUs), to accelerate beam forming. We use two different frameworks for GPUs, CUDA and Open CL, and present results for hardware from different vendors (i.e. AMD and NVIDIA). Additionally, we implement the LOFAR beam former on multi-core CPUs, using Open MP with SSE vector instructions. We use auto-tuning to support different architectures and implementation frameworks, achieving both platform and performance portability. Finally, we compare our results with the production implementation, written in assembly and running on an IBM Blue Gene/P supercomputer. We compare both computational and power efficiency, since power usage is one of the fundamental challenges modern radio telescopes face. Compared to the production implementation, our auto-tuned beam former is 45-50 times faster on GPUs, and 2-8 times more power efficient. Our experimental results lead to the conclusion that GPUs are an attractive solution to accelerate beam forming.

LOFAR is the first of a new generation of radio telescopes.Rather than using expensive dishes, it forms a distributed sensor network that combines the signals from many thousands of simple antennas. Its revolutionary design allows observations in a frequency range that has hardly been studied before.

Recently, there has been a growing interest in academic and commercial environments for video-on-demand (VoD) using peer-to-peer (P2P) technology. Unlike centralized solutions for VoD services, P2P technology lets the clients distribute video content among themselves. In this paper,we propose an analytical model for P2PVoD and we compare that model to a realistic P2PVoD simulator. With our model, parameters that affect the system performance can be observed, and the system stability can be investigated. Our model leads to design rules for achieving a good and stable system performance. This work is, to our knowledge,the first analytical work to model mesh-based P2PVoD.

The main purpose of many current peer-to-peer (P2P) networks is off-line file sharing. However, a potentially very promising use of such networks is to share video streams (e.g., TV programs) in real time. In order to do so, the peers in a P2P network who are interested in the same video stream may employ Application Level Multicasting (ALM). In existing P2P networks, peers may exhibit behavior which is problematic for ALM: they are not always willing to donate resources (free-riding), and they may arrive and depart at a high rate (churn). In this paper we propose the Orchard algorithm for creating and maintaining ALM trees in P2P networks, which deals with both these problems. By employing a technique called Multiple Description Coding, we split a video stream into several substreams. Orchard creates a dynamic spanning tree for each of these substreams in such a way that in the resulting forest, no peer has to forward more substreams than it receives. Based on an analysis of the expected performance of Orchard and on experiments in a real system, we find that Orchard is capable of maintaining a multicast forest, even when peers join and leave the forest at a high rate.

The main purpose of many current peer-to-peer (P2P) networks is off-line file sharing. However, a potentially very promising use of such networks is to share video streams (e.g., TV programs) in real time. In order to do so, the peers in a P2P network who are interested in the same video stream may employ application level multicasting (ALM). In existing P2P networks, peers may exhibit behaviour which is problematic for ALM: peers tend not to donate any resources (free-riding), and they arrive and depart at a high rate (churn). In this paper we propose the orchard algorithm for ALM of video streams in P2P systems, which deal with both these problems. By employing a technique called multiple description coding, we split a video stream into several substreams. Orchard creates a dynamic spanning tree for each of these substreams in such a way that in the re suiting forest, no peer has to forward more substreams than it receives. Our experiments show that orchard is capable of providing a good quality of service to every peer, even when peers join and leave the forest at a high rate

An embedded multiprocessor that can run multiple hard-real-time (HRT) jobs simultaneously has to guarantee that enough resources are available to meet the timing constraints. It is essential that both application model and hardware be tailored to this goal. Moreover, suitable resource allocation and scheduling are needed. This paper proposes a resource allocator that gives guarantees for HRT streaming applications. Because new jobs arrive during operation, resource allocation is performed at run-time. This provides admission control. Resource budget enforcement is handled by local schedulers. We formalize our resource allocation problem and show that it is NP-complete. We developed heuristics to tackle the problem during runtime and evaluated them. A modified First-fit Vector Bin-Packing algorithm provides a good solution; it can allocate 95% of the resources, while handling a large number of job arrivals and departures on a heavily loaded system.