Given the existing heterogeneous processor landscape dominated by CPUs and GPUs, topics such as programming productivity and performance portability have become increasingly important. In this context, an important question refers to how can we develop optimization strategies that cover both CPUs and GPUs. We answer this for fastsg, a library that provides functionality for handling efficiently high-dimensional functions. As it can be employed for compressing and decompressing large-scale simulation data, it finds itself at the core of a computational steering application which serves us as test case. We describe our experience with implementing fastsg's time critical routines for Intel CPUs and Nvidia Fermi GPUs. We show the differences and especially the similarities between our optimization strategies for the two architectures. With regard to our test case for which achieving high speedups is a "must'" for real-time visualization, we report a speedup of up to 6.2x times compared to the state-of-the-art implementation of the sparse grid technique for GPUs.

The name sparse grids denotes a highly space-efficient, grid-based numerical technique to approximate high-dimensional functions. Although employed in a broad spectrum of applications from different fields, there have only been few tries to use it in real time visualization (e.g. [1]), due to complex data structures and long algorithm runtime. In this work we present a novel approach inspired by principles of I/0-efficient algorithms. Locally applied coefficient permutations lead to improved cache performance and facilitate the use of vector registers for our sparse grid benchmark problem hierarchization. Based on the compact data structure proposed for regular sparse grids in [2], we developed a new algorithm that outperforms existing implementations on modern multi-core systems by a factor of 37 for a grid size of 127 million points. For larger problems the speedup is even increasing, and with execution times below 1 s, sparse grids are well-suited for visualization applications. Furthermore, we point out how a broad class of sparse grid algorithms can benefit from our approach.

The sparse grid discretization technique enables a compressed representation of higher-dimensional functions. In its original form, it relies heavily on recursion and complex data structures, thus being far from well-suited for GPUs. In this paper, we describe optimizations that enable us to implement compression and decompression, the crucial sparse grid algorithms for our application, on Nvidia GPUs. The main idea consists of a bijective mapping between the set of points in a multi-dimensional sparse grid and a set of consecutive natural numbers. The resulting data structure consumes a minimum amount of memory. For a 10-dimensional sparse grid with approximately 127 million points, it consumes up to 30 times less memory than trees or hash tables which are typically used. Compared to a sequential CPU implementation, the speedups achieved on GPU are up to 17 for compression and up to 70 for decompression, respectively. We show that the optimizations are also applicable to multicore CPUs.

The high popularity of mobile computing and the increasing number of powerful mobile phones that are able to access high speed data networks pave the way to new scenarios for multimedia applications for mobile devices. Older limitations on network access, displaying capabilities and computing power have been surmounted. The users now have powerful devices and more bandwidth and expect that new, as well as traditional, multimedia applications are available on them. There is a need to offer users a coherent experience and provide multimedia independent of the constrains of the device in which it runs, and the network access technologies used. A way to achieve this is to use a software framework on top of which applications can be built. This framework is able to run on top of a wide variety of mobile platforms and enables a fast and easy deployment of multimedia clients. This paper analyzes the requirements and describes the architecture of Multimedia Open Internet Services and Telecommunication Environment (MONSTER), a framework for mobile multimedia clients currently developed at the Fraunhofer Institute for Open Communication Systems (FOKUS).

In this paper we provide the description of a new mobility service for an IP Multimedia Subsystem (IMS) platform. It optimizes the operation of multimedia services in a wireless heterogeneous environment containing multiple access networks with different cost per bandwidth ratio. It extends the SIP Presence Service with mobility related information, i.e. Media Independent Handover (MIH). The terminals are able to exchange specific handover knowledge with the remote service provisioning infrastructure and to receive commands adapted to the profile of all the parties involved in multimedia sessions, transparent to the actual multimedia service. The system was designed to enable the adaptation of multimedia sessions to the profile of the users and to the momentary network conditions on an end-to-end basis.