Graphics Processing Units (GPUs) have been employed in embedded systems to handle increased amounts of computation and to satisfy the timing requirement. Due to the small feature size, chip aging and within-die parameter variations have been considered to be among the challenging problems for state-of-the-art processors, including GPUs. In order to deal with the process variation, several processors use chip-level guardbanding, which uses the lowest operating frequency that results in a significant chip-level performance drop. Other processors improve their performance efficiency through core-level guardbanding that may use a different operating frequency for each core. Existing aging management techniques are based on the chip-level guardbanding, which assigns the same number of instructions to the cores that have the same aging status. However, in the presence of the process variation, existing aging management techniques have a limitation in minimizing the aging effect because each core has a different amount of stress for the same number of instructions. In order to tackle this problem, we propose a low-overhead aging and process variation aware workload management technique for embedded GPUs. The proposed technique considers the process variation and the current aging status together, and assigns a different number of instructions to clusters to minimize the aging effect in the presence of process variation. Results show that our technique improves the GPU aging in over 95 percent of cases whereas the state-of-the-art compiler-based technique improves the GPU aging in 72.25 percent of cases. Moreover, compared to the compiler-based technique, our technique reduces the performance overhead by 40 percent while achieving almost the same GPU aging improvement.

GPUs have been employed in the embedded systems to handle increased amount of computation and satisfy the timing requirement. Therefore, the lifetime of embedded GPUs is considered one of the most important aspects to ensure functional correctness over a long period of time. Moreover, existing state-of-the-art compiler-based GPU aging management techniques suffer from a considerable amount of performance overhead. In this paper, we propose a low-overhead aging-aware resource management technique. The proposed technique extends the behavior of the existing warp scheduler and the instruction dispatcher to cluster the computational cores and distribute instructions based on the aging information. Compared to when using the original applications, our technique improves the aging of the embedded GPU by 30% on average. In addition, compared to the state-of-the-art GPU aging management technique, our technique reduces the performance overhead by 16.4% on average while improving the aging by 3% on average.

Graphics processing units (GPUs) have been employed in the critical path of applications in embedded systems due to the GPUs' programmability, high-performance, and low power consumption. State-of-the-art GPUs have the capability to process multiple GPU workloads concurrently. Moreover, GPU-based embedded systems have been considered to be essential because of the increased number of throughput-oriented applications and system events. However, existing application scheduling frameworks on a GPU do not have enough flexibility to handle the dynamic behavior of the event-driven applications. This is because in the existing scheduling frameworks: only temporal preemption is considered and one application occupies the GPU at a time. In order to tackle these problems, we propose a novel run-time scheduling framework that considers both temporal and spatial preemptions concurrently. We demonstrate the capability and novelty of our framework compared to the existing scheduling frameworks with realistic benchmark applications and with different execution scenarios. Experimental results show that our scheduling framework is able to guarantee up to 1.37 times as many applications compared to other scheduling frameworks. Moreover, the total amount of timing violation is decreased by up to 54.57%.

GPU architecture has traditionally been used in graphics application because of its enormous computing capability. Moreover, GPU architecture has also been used for general purpose computing in these days. Most of the current scheduling frameworks that are developed to handle GPGPU workload operate sequentially. This is problematic since this sequential approach may not be scalable for real-time systems, which is a consequence of the approach's inability to support preemption. We propose a novel scheduling framework that provides real-time support for the GPGPU platform. In contrast to existing frameworks, our proposed framework considers both concurrent execution of applications on the GPU and mapping between streaming multiprocessors and thread blocks. By considering both concurrent execution and mapping, our framework is able to guarantee timing up to 6.4 times as many applications compared to TimeGraph [9] and Global EDF [5]. In addition, our experimental applications use up to 20% less power under our scheduling framework compared to [5], [9].