Optimizations in stream programming for multimedia applications

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (pages 85-89).Multimedia applications are the most dominant workload in desktop and mobile computing. Such applications regularly process continuous sequences of data and can be naturally represented under the stream programming domain to take take advantage of domain-specific optimizations. Exploiting characteristics specific to multimedia programs can provide further significant impact on performance for this class of programs. This thesis identifies many multimedia applications that maintain induction variable state, which directly inhibits data parallelism for the program. We demonstrates it is essential to recognize and parallelize filters with induction variable state to enable scalable parallelization. We eliminate such state by introducing a new language construct that automatically returns the current iteration number of a target filter. This thesis also exploits the fact that multimedia applications are tolerant in the accuracy of the program output. We apply a memoization technique that exploits this tolerance and the repetitive nature of multimedia data. We provide a runtime system that automatically tunes the memoization capabilities for performance and output quality. These optimizations are implemented in the StreamIt programmming language. The necessity of parallelizing induction variable state and performance improvements and quality control of our memoization technique is demonstrated by a case study of the MPEG benchmark.by Eric Wong.M. Eng

Robust Mobile Visual Recognition System: From Bag of Visual Words to Deep Learning

With billions of images captured by mobile users everyday, automatically recognizing contents in such images has become a particularly important feature for various mobile apps, including augmented reality, product search, visual-based authentication etc. Traditionally, a client-server architecture is adopted such that the mobile client sends captured images/video frames to a cloud server, which runs a set of task-specific computer vision algorithms and sends back the recognition results. However, such scheme may cause problems related to user privacy, network stability/availability and device energy.In this dissertation, we investigate the problem of building a robust mobile visual recognition system that achieves high accuracy, low latency, low energy cost and privacy protection. Generally, we study two broad types of recognition methods: the bag of visual words (BOVW) based retrieval methods, which search the nearest neighbor image to a query image, and the state-of-the-art deep learning based methods, which recognize a given image using a trained deep neural network. The challenges of deploying BOVW based retrieval methods include: size of indexed image database, query latency, feature extraction efficiency and re-ranking performance. To address such challenges, we first proposed EMOD which enables efficient on-device image retrieval on a downloaded context-dependent partial image database. The efficiency is achieved by analyzing the BOVW processing pipeline and optimizing each module with algorithmic improvement.Recent deep learning based recognition approaches have been shown to greatly exceed the performance of traditional approaches. We identify several challenges of applying deep learning based recognition methods on mobile scenarios, namely energy efficiency and privacy protection for real-time visual processing, and mobile visual domain biases. Thus, we proposed two techniques to address them, (i) efficiently splitting the workload across heterogeneous computing resources, i.e., mobile devices and the cloud using our Moca framework, and (ii) using mobile visual domain adaptation as proposed in our collaborative edge-mediated platform DeepCham. Our extensive experiments on large-scale benchmark datasets and off-the-shelf mobile devices show our solutions provide better results than the state-of-the-art solutions

Affective Image Content Analysis: Two Decades Review and New Perspectives

Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this survey, we will comprehensively review the development of AICA in the recent two decades, especially focusing on the state-of-the-art methods with respect to three main challenges -- the affective gap, perception subjectivity, and label noise and absence. We begin with an introduction to the key emotion representation models that have been widely employed in AICA and description of available datasets for performing evaluation with quantitative comparison of label noise and dataset bias. We then summarize and compare the representative approaches on (1) emotion feature extraction, including both handcrafted and deep features, (2) learning methods on dominant emotion recognition, personalized emotion prediction, emotion distribution learning, and learning from noisy data or few labels, and (3) AICA based applications. Finally, we discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.Comment: Accepted by IEEE TPAM

Computing Competencies for Undergraduate Data Science Curricula: ACM Data Science Task Force

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level. There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science programs, employers who hope to hire a workforce with knowledge and experience in data science, as well as individuals and professional societies representing the fields of computing, statistics, machine learning, computational biology, computational social sciences, digital humanities, and others. There is a shared desire to form a broad interdisciplinary definition of data science and to develop curriculum guidance for degree programs in data science. This volume builds upon the important work of other groups who have published guidelines for data science education. There is a need to acknowledge the definition and description of the individual contributions to this interdisciplinary field. For instance, those interested in the business context for these concepts generally use the term “analytics”; in some cases, the abbreviation DSA appears, meaning Data Science and Analytics. This volume is the third draft articulation of computing-focused competencies for data science. It recognizes the inherent interdisciplinarity of data science and situates computing-specific competencies within the broader interdisciplinary space

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Heterogeneous parallel virtual machine: A portable program representation and compiler for performance and energy optimizations on heterogeneous parallel systems

Programming heterogeneous parallel systems, such as the SoCs (System-on-Chip) on mobile and edge devices is extremely difficult; the diverse parallel hardware they contain exposes vastly different hardware instruction sets, parallelism models and memory systems. Moreover, a wide range of diverse hardware and software approximation techniques are available for applications targeting heterogeneous SoCs, further exacerbating the programmability challenges. In this thesis, we alleviate the programmability challenges of such systems using flexible compiler intermediate representation solutions, in order to benefit from the performance and superior energy efficiency of heterogeneous systems. First, we develop Heterogeneous Parallel Virtual Machine (HPVM), a parallel program representation for heterogeneous systems, designed to enable functional and performance portability across popular parallel hardware. HPVM is based on a hierarchical dataflow graph with side effects. HPVM successfully supports three important capabilities for programming heterogeneous systems: a compiler intermediate representation (IR), a virtual instruction set (ISA), and a basis for runtime scheduling. We use the HPVM representation to implement an HPVM prototype, defining the HPVM IR as an extension of the Low Level Virtual Machine (LLVM) IR. Our results show comparable performance with optimized OpenCL kernels for the target hardware from a single HPVM representation using translators from HPVM virtual ISA to native code, IR optimizations operating directly on the HPVM representation, and the capability for supporting flexible runtime scheduling schemes from a single HPVM representation. We extend HPVM to ApproxHPVM, introducing hardware-independent approximation metrics in the IR to enable maintaining accuracy information at the IR level and mapping of application-level end-to-end quality metrics to system level "knobs". The approximation metrics quantify the acceptable accuracy loss for individual computations. Application programmers only need to specify high-level, and end-to-end, quality metrics, instead of detailed parameters for individual approximation methods. The ApproxHPVM system then automatically tunes the accuracy requirements of individual computations and maps them to approximate hardware when possible. ApproxHPVM results show significant performance and energy improvements for popular deep learning benchmarks. Finally, we extend to ApproxHPVM to ApproxTuner, a compiler and runtime system for approximation. ApproxTuner extends ApproxHPVM with a wide range of hardware and software approximation techniques. It uses a three step approximation tuning strategy, a combination of development-time, install-time, and dynamic tuning. Our strategy ensures software portability, even though approximations have highly hardware-dependent performance, and enables efficient dynamic approximation tuning despite the expensive offline steps. ApproxTuner results show significant performance and energy improvements across 7 Deep Neural Networks and 3 image processing benchmarks, and ensures that high-level end-to-end quality specifications are satisfied during adaptive approximation tuning