Unleashing Fine-Grained Parallelism on Embedded Many-Core Accelerators with Lightweight OpenMP Tasking

In recent years, programmable many-core accelerators (PMCAs) have been introduced in embedded systems to satisfy stringent performance/Watt requirements. This has increased the urge for programming models capable of effectively leveraging hundreds to thousands of processors. Task-based parallelism has the potential to provide such capabilities, offering high-level abstractions to outline abundant and irregular parallelism in embedded applications. However, efficiently supporting this programming paradigm on embedded PMCAs is challenging, due to the large time and space overheads it introduces. In this paper we describe a lightweight OpenMP tasking runtime environment (RTE) design for a state-of-the-art embedded PMCA, the Kalray MPPA 256. We provide an exhaustive characterization of the costs of our RTE, considering both synthetic workload and real programs, and we compare to several other tasking RTEs. Experimental results confirm that our solution achieves near-ideal parallelization speedups for tasks as small as 5K cycles, and an average speedup of 12 × for real benchmarks, which is 60% higher than what we observe with the original Kalray OpenMP implementation

Efficient implementation of resource-constrained cyber-physical systems using multi-core parallelism

The quest for more performance of applications and systems became more challenging in the recent years. Especially in the cyber-physical and mobile domain, the performance requirements increased significantly. Applications, previously found in the high-performance domain, emerge in the area of resource-constrained domain. Modern heterogeneous high-performance MPSoCs provide a solid foundation to satisfy the high demand. Such systems combine general processors with specialized accelerators ranging from GPUs to machine learning chips. On the other side of the performance spectrum, the demand for small energy efficient systems exposed by modern IoT applications increased vastly. Developing efficient software for such resource-constrained multi-core systems is an error-prone, time-consuming and challenging task. This thesis provides with PA4RES a holistic semiautomatic approach to parallelize and implement applications for such platforms efficiently. Our solution supports the developer to find good trade-offs to tackle the requirements exposed by modern applications and systems. With PICO, we propose a comprehensive approach to express parallelism in sequential applications. PICO detects data dependencies and implements required synchronization automatically. Using a genetic algorithm, PICO optimizes the data synchronization. The evolutionary algorithm considers channel capacity, memory mapping, channel merging and flexibility offered by the channel implementation with respect to execution time, energy consumption and memory footprint. PICO's communication optimization phase was able to generate a speedup almost 2 or an energy improvement of 30% for certain benchmarks. The PAMONO sensor approach enables a fast detection of biological viruses using optical methods. With a sophisticated virus detection software, a real-time virus detection running on stationary computers was achieved. Within this thesis, we were able to derive a soft real-time capable virus detection running on a high-performance embedded system, commonly found in today's smart phones. This was accomplished with smart DSE algorithm which optimizes for execution time, energy consumption and detection quality. Compared to a baseline implementation, our solution achieved a speedup of 4.1 and 87\% energy savings and satisfied the soft real-time requirements. Accepting a degradation of the detection quality, which still is usable in medical context, led to a speedup of 11.1. This work provides the fundamentals for a truly mobile real-time virus detection solution. The growing demand for processing power can no longer satisfied following well-known approaches like higher frequencies. These so-called performance walls expose a serious challenge for the growing performance demand. Approximate computing is a promising approach to overcome or at least shift the performance walls by accepting a degradation in the output quality to gain improvements in other objectives. Especially for a safe integration of approximation into existing application or during the development of new approximation techniques, a method to assess the impact on the output quality is essential. With QCAPES, we provide a multi-metric assessment framework to analyze the impact of approximation. Furthermore, QCAPES provides useful insights on the impact of approximation on execution time and energy consumption. With ApproxPICO we propose an extension to PICO to consider approximate computing during the parallelization of sequential applications

Improving MPI Threading Support for Current Hardware Architectures

Threading support for Message Passing Interface (MPI) has been defined in the MPI standard for more than twenty years. While many standard-compliance MPI implementations fully support multithreading, the threading support in MPI still cannot provide the optimal performance on the same level as the non-threading environment. The performance disparity leads to low adoption rate from applications, and eventually, lesser interest in optimizing MPI threading support. However, with the current advancement in computation hardware, the number of CPU core per packet is growing drastically. Using shared-memory MPI communication has become more costly. MPI threading without local communication is one of the alternatives and the some interests are shifting back toward threading to MPI.In this work, we investigate different approaches to leverage the power of thread parallelism and tools to help us to raise the multi-threaded MPI performance to reasonable level. We propose a novel multi-threaded MPI benchmark with multiple communication patterns to stress multiple points of the MPI implementation, with the ability to switch between using MPI process and threads for quick comparison between two modes. Enabling the us, and the others MPI developers to stress test their implementation design.We address the interoperability between MPI implementation and threading frameworks by introducing the thread-synchronization object, an object that gives the MPI implementation more control over user-level thread, allowing for more thread utilization in MPI. In our implementation, the synchronization object relieves the lock contention on the internal progress engine and able to achieve up to 7x the performance of the original implementation. Moving forward, we explore the possibility of harnessing the true thread concurrency. We proposed several strategies to address the bottlenecks in MPI implementation. From our evaluation, with our novel threading optimization, we can achieve up to 22x the performance comparing to the legacy MPI designs

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma