Jason Maassen

eyeDentify: Multimedia Cyber Foraging from a Smartphone

The recent introduction of smartphones has resulted in an explosion of innovative mobile applications. The computational requirements of many of these applications, however, can not be met by the smartphone itself. The compute power of the smartphone can be enhanced by distributing the application over other compute resources. Existing solutions comprise of a light weight client running on the smartphone and a heavy weight compute server running on, for example, a cloud. This places the user in a dependent position, however, because the user only controls the client application. In this paper, we follow a different model, called cyber foraging, that gives users full control over all parts of the application. We have implemented the model using the Ibis middleware. We evaluate the model using an innovative application in the domain of multimedia computing, and show that cyber foraging increases the applications responsiveness and accuracy whilst decreasing its energy usage.

Fault-tolerance, malleability and migration for divide-and-conquer applications on the grid

Grid applications have to cope with dynamically changing computing resources as machines may crash or be claimed by other, higher-priority applications. In this paper, we propose a mechanism that enables fault-tolerance, malleability (e.g. the ability to cope with a dynamically changing number of processors) and migration for divide-and-conquer applications on the grid. The novelty of our approach is restructuring the computation tree, which eliminates redundant computation and salvages partial results computed by the processors leaving the computation. This enables the applications to adapt to dynamically changing numbers of processors and to migrate the computation without loss of work. Our mechanism is easy to implement and deploy in grid environment. The overhead it incurs is close to zero. We have implemented our mechanism in the Satin system. We have evaluated the performance of our system on the DAS-2 wide-area system and on the testbed of the European GridLab project.

Performance Models for CPU-GPU Data Transfers

Many GPU applications perform data transfers to and from GPU memory at regular intervals. For example because the data does not fit into GPU memory or because of internode communication at the end of each time step. Overlapping GPU computation with CPU-GPU communication can reduce the costs of moving data. Several different techniques exist for transferring data to and from GPU memory and for overlapping those transfers with GPU computation. It is currently not known when to apply which method. Implementing and benchmarking each method is often a large programming effort and not feasible. To solve these issues and to provide insight in the performance of GPU applications, we propose an analytical performance model that includes PCIe transfers and overlapping computation and communication. Our evaluation shows that the performance models are capable of correctly classifying the relative performance of the different implementations.

Experiences with Fine-Grained Distributed Supercomputing on a 10G Testbed

This paper shows how lightpath-based networks can allow challenging, fine-grained parallel supercomputing applications to be run on a grid, using parallel retrograde analysis on DAS-3 as a case study. Detailed performance analysis shows that several problems arise that are not present on tightly-coupled systems like clusters. In particular, flow control, asynchronous communication, and host- level communication overheads become new obstacles. By optimizing these aspects, however, a 10 G grid can obtain high performance for this type of communication-intensive application. The class of large-scale distributed applications suitable for running on a grid is therefore larger than previously thought realistic.

A Jungle Computing approach to common image source identification in large collections of images

Abstract Analyzing digital images is an important investigation in forensics with the ever increasing number of images from computers and smartphones. In this article we aim to advance the state-of-the-art in common image source identification (which images originate from the same source camera). To this end, we present two types of applications for different goals that make use of a) a modern Desktop computer with a GPU and b) highly heterogeneous cluster computers with many different kinds of GPUs, something we call computing jungles. The first application targets medium-scale investigations, for example within a crime laboratory, the second application is targeted at large-scale investigations, for example within institutions. We advance the state-of-the-art by 1) explaining in detail how we obtain the performance to 2) support large databases of images in reasonable time while 3) not giving up accuracy. Moreover, we do not apply filtering ensuring that 4) our results are highly reproducible.