Multimedia applications are becoming increasingly important for a large class of general-purpose processors. Contemporary media applications are highly complex and demand high performance. A distinctive feature of these applications is that they have significant parallelism, including thread-, data-, and instruction-level parallelism, that is potentially well-aligned with the increasing parallelism supported by emerging multi-core architectures. Designing systems to meet the demands of these applications therefore requires a benchmark suite comprising these complex applications and that exposes the parallelism present in them. This paper makes two contributions. First, it presents ALPBench, a publicly available benchmark suite that pulls together five complex media applications from various sources: speech recognition (CMU Sphinx 3), face recognition (CSU), ray tracing (Tachyon), MPEG-2 encode (MSSG), and MPEG-2 decode (MSSG). We have modified the original applications to expose thread-level and datalevel parallelism using POSIX threads and sub-word SIMD (Intel’s SSE2) instructions respectively. Second, the paper provides a performance characterization of the ALPBench benchmarks, with a focus on parallelism. Such a characterization is useful for architects and compiler writers for designing systems and compiler optimizations for these applications. 1.

Multimedia applications are becoming increasingly important for a large class of general-purpose processors. Contemporary media applications are highly complex and demand high performance. A distinctive feature of these applications is that they have significant parallelism, including thread-, data-, and instruction-level parallelism, that is potentially well-aligned with the increasing parallelism supported by emerging multicore architectures. Designing systems to meet the demands of these applications therefore requires a benchmark suite comprising these complex applications and that exposes the parallelism present in them. This thesis makes three main contributions. First, it presents ALPBench, a publicly released benchmark suite that pulls together five complex media applications from various sources: speech recognition (CMU Sphinx 3.3), face recognition (CSU), ray tracing (Tachyon), MPEG-2 encode (MSSG), and MPEG-2 decode (MSSG). We have modified the original applications to expose thread-level parallelism using POSIX threads and data-level parallelism using Intel’s SSE2 instructions and vector extensions. Second, the thesis provides a performance characterization of the ALPBench benchmarks, with a focus on parallelism. Such a characterization is useful for architects and compiler writers for designing systems and compiler optimizations for these applications.

Abstract- The e-Learning technologies makes users capable of accessing a large amount of learning objects created in various parts of the world and across many cultures. Due to the semantic heterogeneity of learning objects built within different cultural contexts, the use of these learning objects by learners is often ineffective. However, developing learning objects in a way that would not only allow their reuse, but promote their repurposing, is a challenging problem. This article proposes an ontology based framework for repurposing multimedia learning object components. Unlike the usual practice where multimedia learning object components are assembled manually, the proposed framework enables on-the-fly access and repurposing of learning object components. The framework supports two processes: the decomposition of learning objects into their components as well as the automatic assembly of these components in real-world applications. For now, the framework supports slide presentations. As an application, we will present in this paper the integration of this functionality in SVG. Key-Words- Learning Objects, Multimedia Learning Objects, SVG 1.