Low-power microcontrollers lack some of the hardware features and memory resources that enable multiprogrammable systems. Accordingly, microcontroller-based operating systems have not provided important features like ...
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ISBN:
(纸本)9781450350853
Low-power microcontrollers lack some of the hardware features and memory resources that enable multiprogrammable systems. Accordingly, microcontroller-based operating systems have not provided important features like fault isolation, dynamic memory allocation, and flexible concurrency. However, an emerging class of embedded applications are software platforms, rather than single purpose devices, and need these multiprogramming features. Tock, a new operating system for low-power platforms, takes advantage of limited hardware-protection mechanisms as well as the type-safety features of the Rust programming language to provide a multiprogramming environment for microcontrollers. Tock isolates software faults, provides memory protection, and efficiently manages memory for dynamic application workloads written in any language. It achieves this while retaining the dependability requirements of long-running applications.
NASA Technical Reports Server (Ntrs) 19900017242: Single-Pass Memory System Evaluation for multiprogramming Workloads by NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 19900017242: Single-Pass Memory System Evaluation for multiprogramming Workloads by NASA Technical Reports Server (Ntrs); published by
This paper deals with retrial queueing models having an unlimited/a finite orbit capacity with control retrial policy of a multiprogramming-multiprocessor computer network system. Under the Markovian assumptions and l...
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This paper deals with retrial queueing models having an unlimited/a finite orbit capacity with control retrial policy of a multiprogramming-multiprocessor computer network system. Under the Markovian assumptions and light-traffic condition, the steady-state probabilities of the number of programs in the system and the mean number of programs in the orbit are studied using matrix geometric/analytic methods. The expressions for the Laplace-Stieglitz transforms of the busy period and the waiting time are obtained. The probability generating function for the number of retrials made by a tagged program is also derived. Some interesting performance measures of the system and the various moments of quantities of interest are discussed. Finally, extensive numerical results are illustrated to reveal the impact of the system parameters on the performance measures.
As technology scales, GPUs are forecasted to incorporate an ever-increasing amount of computing resources to support thread-level parallelism. But even with the best effort, exposing massive thread-level parallelism f...
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ISBN:
(纸本)9781467389471
As technology scales, GPUs are forecasted to incorporate an ever-increasing amount of computing resources to support thread-level parallelism. But even with the best effort, exposing massive thread-level parallelism from a single GPU kernel, particularly from general purpose applications, is going to be a difficult challenge. In some cases, even if there is sufficient thread-level parallelism in a kernel, there may not be enough available memory bandwidth to support such massive concurrent thread execution. Hence, GPU resources may be underutilized as more general purpose applications are ported to execute on GPUs. In this paper, we explore multiprogramming GPUs as a way to resolve the resource underutilization issue. There is a growing hardware support for multiprogramming on GPUs. Hyper-Q has been introduced in the Kepler architecture which enables multiple kernels to be invoked via tens of hardware queue streams. Spatial multitasking has been proposed to partition GPU resources across multiple kernels. But the partitioning is done at the coarse granularity of streaming multiprocessors (SMs) where each kernel is assigned to a subset of SMs. In this paper, we advocate for partitioning a single SM across multiple kernels, which we term as intra-SM slicing. We explore various intra-SM slicing strategies that slice resources within each SM to concurrently run multiple kernels on the SM. Our results show that there is not one intra-SM slicing strategy that derives the best performance for all application pairs. We propose Warped-Slicer, a dynamic intra-SM slicing strategy that uses an analytical method for calculating the SM resource partitioning across different kernels that maximizes performance. The model relies on a set of short online profile runs to determine how each kernel's performance varies as more thread blocks from each kernel are assigned to an SM. The model takes into account the interference effect of shared resource usage across multiple kernels. The m
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