Supporting Preemptive Task Executions and Memory Copies in GPGPUs

GPGPUs (General Purpose Graphic Processing Units) provide massive computational power. However, applying GPGPU technology to real-time computing is challenging due to the non-preemptive nature of GPGPUs. Especially, a job running in a GPGPU or a data copy between a GPGPU and CPU is non-preemptive. As a result, a high priority job arriving in the middle of a low priority job execution or memory copy suffers from priority inversion. To address the problem, we present a new lightweight approach to supporting preemptive memory copies and job executions in GPGPUs. Moreover, in our approach, a GPGPU job and memory copy between a GPGPU and the hosting CPU are run concurrently to enhance the responsiveness. To show the feasibility of our approach, we have implemented a prototype system for preemptive job executions and data copies in a GPGPU. The experimental results show that our approach can bound the response times in a reliable manner. In addition, the response time of our approach is significantly shorter than those of the unmodified GPGPU runtime system that supports no preemption and an advanced GPGPU model designed to support prioritization and performance isolation via preemptive data copies

Disengaged Scheduling for Fair, Protected Access to Fast Computational Accelerators

Today’s operating systems treat GPUs and other computational accelerators as if they were simple devices, with bounded and predictable response times. With accelerators assuming an increasing share of the workload on modern machines, this strategy is already problematic, and likely to become untenable soon. If the operating system is to enforce fair sharing of the machine, it must assume responsibility for accelerator scheduling and resource management. Fair, safe scheduling is a particular challenge on fast accelerators, which allow applications to avoid kernel-crossing overhead by interacting directly with the device. We propose a disengaged scheduling strategy in which the kernel intercedes between applications and the accelerator on an infrequent basis, to monitor their use of accelerator cycles and to determine which applications should be granted access over the next time interval. Our strategy assumes a well defined, narrow interface exported by the accelerator. We build upon such an interface, systematically inferred for the latest Nvidia GPUs. We construct several example schedulers, including Disengaged Timeslice with overuse control that guarantees fairness and Disengaged Fair Queueing that is effective in limiting resource idleness, but probabilistic. Both schedulers ensure fair sharing of the GPU, even among uncooperative or adversarial applications; Disengaged Fair Queueing incurs a 4 % overhead on average (max 18%) compared to direct devic

Bio-inspired retinal optic flow perception in robotic navigation

This thesis concerns the bio-inspired visual perception of motion with emphasis on locomotion targeting robotic systems. By continuously registering moving visual features in the human retina, a sensation of a visual flow cue is created. An interpretation of visual flow cues forms a low-level motion perception more known as retinal optic flow. Retinal optic flow is often mentioned and credited in human locomotor research but only in theory and simulated environments so far. Reconstructing the retinal optic flow fields using existing methods of estimating optic flow and experimental data from naive test subjects provides further insight into how it interacts with intermittent control behavior and dynamic gazing. The retinal optic flow is successfully demonstrated during a vehicular steering task scenario and further supports the idea that humans may use such perception to aid their ability to correct their steering during navigation.To achieve the reconstruction and estimation of the retinal optic flow, a set of optic flow estimators were fairly and systematically evaluated on the criteria on run-time predictability and reliability, and performance accuracy. A formalized methodology using containerization technology for performing the benchmarking was developed to generate the results. Furthermore, the readiness in road vehicles for the adoption of modern robotic software and related software processes were investigated. This was done with special emphasis on real-time computing and introducing containerization and microservice design paradigm. By doing so, continuous integration, continuous deployment, and continuous experimentation were enabled in order to aid further development and research. With the method of estimating retinal optic flow and its interaction with intermittent control, a more complete vision-based bionic steering control model is to be proposed and tested in a live robotic system

Sharing Non-Processor Resources in Multiprocessor Real-Time Systems

Computing devices are increasingly being leveraged in cyber-physical systems, in which computing devices sense, control, and interact with the physical world. Associated with many such real-world interactions are strict timing constraints, which if unsatisfied, can lead to catastrophic consequences. Modern examples of such timing constraints are prevalent in automotive systems, such as airbag controllers, anti-lock brakes, and new autonomous features. In all of these examples, a failure to correctly respond to an event in a timely fashion could lead to a crash, damage, injury and even loss of life. Systems with imperative timing constraints are called real-time systems, and are broadly the subject of this dissertation. Much previous work on real-time systems and scheduling theory assumes that computing tasks are independent, i.e., the only resource they share is the platform upon which they are executed. In practice, however, tasks share many resources, ranging from more overt resources such as shared memory objects, to less overt ones, including data buses and other hardware and I/O devices. Accesses to some such resources must be synchronized to ensure safety, i.e., logical correctness, while other resources may exhibit better run-time performance if accesses are explicitly synchronized. The goal of this dissertation was to develop new synchronization algorithms and associated analysis techniques that can be used to synchronize access to many classes of resources, while improving the overall resource utilization, specifically as measured by real-time schedulability. Towards that goal, the Real-Time Nested Locking Protocol (RNLP), the first multiprocessor real-time locking protocol that supports lock nesting or fine-grained locking is proposed and analyzed. Furthermore, the RNLP is extended to support reader/writer locking, as well as k-exclusion locking. All presented RNLP variants are proven optimal. Furthermore, experimental results demonstrate the schedulability-related benefits of the RNLP. Additionally, three new synchronization algorithms are presented, which are specifically motivated by the need to manage shared hardware resources to improve real-time predictability. Furthermore, two new classes of shared resources are defined, and the first synchronization algorithms for them are proposed. To analyze these new algorithms, a novel analysis technique called idleness analysis is presented, which can be used to incorporate the effects of blocking into schedulability analysis.Doctor of Philosoph