An FPGA implementation of an investigative many-core processor, Fynbos : in support of a Fortran autoparallelising software pipeline

Includes bibliographical references.In light of the power, memory, ILP, and utilisation walls facing the computing industry, this work examines the hypothetical many-core approach to finding greater compute performance and efficiency. In order to achieve greater efficiency in an environment in which Moore’s law continues but TDP has been capped, a means of deriving performance from dark and dim silicon is needed. The many-core hypothesis is one approach to exploiting these available transistors efficiently. As understood in this work, it involves trading in hardware control complexity for hundreds to thousands of parallel simple processing elements, and operating at a clock speed sufficiently low as to allow the efficiency gains of near threshold voltage operation. Performance is there- fore dependant on exploiting a new degree of fine-grained parallelism such as is currently only found in GPGPUs, but in a manner that is not as restrictive in application domain range. While removing the complex control hardware of traditional CPUs provides space for more arithmetic hardware, a basic level of control is still required. For a number of reasons this work chooses to replace this control largely with static scheduling. This pushes the burden of control primarily to the software and specifically the compiler, rather not to the programmer or to an application specific means of control simplification. An existing legacy tool chain capable of autoparallelising sequential Fortran code to the degree of parallelism necessary for many-core exists. This work implements a many-core architecture to match it. Prototyping the design on an FPGA, it is possible to examine the real world performance of the compiler-architecture system to a greater degree than simulation only would allow. Comparing theoretical peak performance and real performance in a case study application, the system is found to be more efficient than any other reviewed, but to also significantly under perform relative to current competing architectures. This failing is apportioned to taking the need for simple hardware too far, and an inability to implement static scheduling mitigating tactics due to lack of support for such in the compiler

Sharing GPUs for Real-Time Autonomous-Driving Systems

Autonomous vehicles at mass-market scales are on the horizon. Cameras are the least expensive among common sensor types and can preserve features such as color and texture that other sensors cannot. Therefore, realizing full autonomy in vehicles at a reasonable cost is expected to entail computer-vision techniques. These computer-vision applications require massive parallelism provided by the underlying shared accelerators, such as graphics processing units, or GPUs, to function “in real time.” However, when computer-vision researchers and GPU vendors refer to “real time,” they usually mean “real fast”; in contrast, certifiable automotive systems must be “real time” in the sense of being predictable. This dissertation addresses the challenging problem of how GPUs can be shared predictably and efficiently for real-time autonomous-driving systems. We tackle this challenge in four steps. First, we investigate NVIDIA GPUs with respect to scheduling, synchronization, and execution. We conduct an extensive set of experiments to infer NVIDIA GPU scheduling rules, which are unfortunately undisclosed by NVIDIA and are beyond access owing to their closed-source software stack. We also expose a list of pitfalls pertaining to CPU-GPU synchronization that can result in unbounded response times of GPU-using applications. Lastly, we examine a fundamental trade-off for designing real-time tasks under different execution options. Overall, our investigation provides an essential understanding of NVIDIA GPUs, allowing us to further model and analyze GPU tasks. Second, we develop a new model and conduct schedulability analysis for GPU tasks. We extend the well-studied sporadic task model with additional parameters that characterize the parallel execution of GPU tasks. We show that NVIDIA scheduling rules are subject to fundamental capacity loss, which implies a necessary total utilization bound. We derive response-time bounds for GPU task systems that satisfy our schedulability conditions. Third, we address an industrial challenge of supplying the throughput performance of computer-vision frameworks to support adequate coverage and redundancy offered by an array of cameras. We re-think the design of convolution neural network (CNN) software to better utilize hardware resources and achieve increased throughput (number of simultaneous camera streams) without any appreciable increase in per-frame latency (camera to CNN output) or reduction of per-stream accuracy. Fourth, we apply our analysis to a finer-grained graph scheduling of a computer-vision standard, OpenVX, which explicitly targets embedded and real-time systems. We evaluate both the analytical and empirical real-time performance of our approach.Doctor of Philosoph

Real-Time Scheduling for GPUs with Applications in Advanced Automotive Systems

Self-driving cars, once constrained to closed test tracks, are beginning to drive alongside human drivers on public roads. Loss of life or property may result if the computing systems of automated vehicles fail to respond to events at the right moment. We call such systems that must satisfy precise timing constraints “real-time systems.” Since the 1960s, researchers have developed algorithms and analytical techniques used in the development of real-time systems; however, this body of knowledge primarily applies to traditional CPU-based platforms. Unfortunately, traditional platforms cannot meet the computational requirements of self-driving cars without exceeding the power and cost constraints of commercially viable vehicles. We argue that modern graphics processing units, or GPUs, represent a feasible alternative, but new algorithms and analytical techniques must be developed in order to integrate these uniquely constrained processors into a real-time system. The goal of the research presented in this dissertation is to discover and remedy the issues that prevent the use of GPUs in real-time systems. To overcome these issues, we design and implement a real-time multi-GPU scheduler, called GPUSync. GPUSync tightly controls access to a GPU’s computational and DMA processors, enabling simultaneous use despite potential limitations in GPU hardware. GPUSync enables tasks to migrate among GPUs, allowing new classes of real-time multi-GPU computing platforms. GPUSync employs heuristics to guide scheduling decisions to improve system efficiency without risking violations in real-time constraints. GPUSync may be paired with a wide variety of common real-time CPU schedulers. GPUSync supports closed-source GPU runtimes and drivers without loss in functionality. We evaluate GPUSync with both analytical and runtime experiments. In our analytical experiments, we model and evaluate over fifty configurations of GPUSync. We determine which configurations support the greatest computational capacity while maintaining real-time constraints. In our runtime experiments, we execute computer vision programs similar to those found in automated vehicles, with and without GPUSync. Our results demonstrate that GPUSync greatly reduces jitter in video processing. Research into real-time systems with GPUs is a new area of study. Although there is prior work on such systems, no other GPU scheduling framework is as comprehensive and flexible as GPUSync.Doctor of Philosoph

Energy Efficient Servers

Computer scienc

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest

Energy Efficient Servers

Computer scienc