The integration of graphics processing units (GPUs) into real-time systems has recently become an active area of research. However, prior research on this topic has failed to produce real-time GPU allocation methods that fully exploit the available parallelism in GPU-enabled systems. In this paper, a GPU management framework called GPUSync is described that enables increased parallelism while providing predictable real-time behavior. GPUSync can be applied in multi-GPU real-time systems, is cognizant of the system bus architecture, and fully exposes the parallelism offered by modern GPUs, even when closed-source GPU drivers are used. Schedulability tests presented herein that incorporate empirically measured overheads, including those due to bus contention, demonstrate that GPUSync offers real-time predictability and performs well in practice.

Graphics processing units, GPUs, are powerful processors that can offer significant performance advantages over traditional CPUs. The last decade has seen rapid advancement in GPU computational power and generality. Recent technologies make it possible to use GPUs as co-processors to the CPU. The performance advantages of GPUs can be great, often outperforming traditional CPUs by orders of magnitude. While the motivations for developing systems with GPUs are clear, little research in the real-time systems field has been done to integrate GPUs into real-time multiprocessor systems. We present two real-time analysis methods, addressing realworld platform constraints, for such an integration into a soft real-time multiprocessor system and show that a GPU can be exploited to achieve greater levels of total system performance.

Graphics processing units (GPUs) are becoming increasingly important in today's platforms as their increased generality allows for them to be used as powerful co-processors. In previous work, we have found that GPUs may be integrated into real-time systems through the treatment of GPUs as shared resources, allocated to real-time tasks through mutual exclusion locking protocols. In this paper, we present an optimal k-exclusion locking protocol for globally-scheduled job-level static-priority (JLSP) systems. This protocol may be used to manage a pool of GPU resources in such systems.

Graphics processing units (GPUs) are becoming increasingly important in today's platforms as their increased generality allows for them to be used as powerful coprocessors. In this paper, we explore possible applications for GPUs in real-time systems, discuss the limitations and constraints imposed by current GPU technology, and present a summary of our research addressing many such constraints.

Real-time locking protocols employ progress mechanism(s) to ensure that resource-holding jobs are scheduled. These mechanisms are required to bound the duration of priorityinversion blocking (pi-blocking) for jobs sharing resources. Examples of such progress mechanisms include priority inheritance and priority donation. Unfortunately, some progress mechanisms can cause any job, including those that never request shared resources, to be blocked upon job release. This paper presents a variant of priority donation for globally-scheduled systems that only causes blocking for jobs waiting for shared resources. Additionally, this variant of priority donation is employed to construct a new suspension-based locking protocol called the replicarequest donation global locking protocol (R 2 DGLP ), which is optimal for both mutex and k-exclusion (i.e., multiresource) locks. This work is motivated by multicore systems where tasks may share I/O devices (e.g., GPUs) where critical sections can be long. In such applications, progress mechanisms that cause jobs that do not access I/O devices to be blocked to ensure progress can be detrimental from a schedulability perspective.

Abstract-Architectures in which multicore chips are augmented with graphics processing units (GPUs) have great potential in many domains in which computationally intensive real-time workloads must be supported. However, unlike standard CPUs, GPUs are treated as I/O devices and require the use of interrupts to facilitate communication with CPUs. Given their disruptive nature, interrupts must be dealt with carefully in real-time systems. With GPU-driven interrupts, such disruptiveness is further compounded by the closed-source nature of GPU drivers. In this paper, such problems are considered and a solution is presented in the form of an extension to LITMUS RT called klmirqd. The design of klmirqd targets systems with multiple CPUs and GPUs. In such settings, interruptrelated issues arise that have not been previously addressed.

Dataflow software architectures are prevalent in prototypes of advanced automotive systems, for both driver-assisted and autonomous driving. Safety constraints of these systems necessitate real-time performance guarantees. Automotive prototypes often ensure such constraints through overprovisioning and dedicated hardware; however, a commercially viable system must utilize as few low-cost multicore processors as possible to meet size, weight, and power constraints. In short, these platforms must do more with less. To this end, we develop cache-aware and overhead-cognizant scheduling techniques that lessen guaranteed response times without unnecessarily constraining platform utilization. We implement these techniques in PGM RT , a portable middleware framework for managing real-time dataflow applications on multicore platforms. The efficacy of our techniques is demonstrated through overhead-aware schedulability experiments and runtime observations. Results for our test platform show that cache-aware clustered scheduling outperforms naïve partitioned and global approaches in terms of schedulability and end-to-end response times of dataflows.

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