The deployment of real-time workloads on commercial off-the-shelf (COTS) hardware is attractive, as it reduces the cost and time-to-market of new products. Most modern high-end embedded SoCs rely on a heterogeneous design, coupling a general-purpose multi-core CPU to a massively parallel accelerator, typically a programmable GPU, sharing a single global DRAM. However, because of non-predictable hardware arbiters designed to maximize average or peak performance, it is very difficult to provide timing guarantees on such systems. In this work we present our ongoing work on GPUguard, a software technique that predictably arbitrates main memory usage in heterogeneous SoCs. A prototype implementation for the NVIDIA Tegra TX1 SoC shows that GPUguard is able to reduce the adverse effects of memory sharing, while retaining a high throughput on both the CPU and the accelerator.

GPUguard: Towards supporting a predictable execution model for heterogeneous SoC / Forsberg, Bjorn; Marongiu, Andrea; Benini, Luca. - ELETTRONICO. - (2017), pp. 318-321. (Intervento presentato al convegno 20th Design, Automation and Test in Europe, DATE 2017 tenutosi a SwissTech Convention Center, che nel 2017) [10.23919/DATE.2017.7927008].

GPUguard: Towards supporting a predictable execution model for heterogeneous SoC

Marongiu, Andrea;
2017

Abstract

The deployment of real-time workloads on commercial off-the-shelf (COTS) hardware is attractive, as it reduces the cost and time-to-market of new products. Most modern high-end embedded SoCs rely on a heterogeneous design, coupling a general-purpose multi-core CPU to a massively parallel accelerator, typically a programmable GPU, sharing a single global DRAM. However, because of non-predictable hardware arbiters designed to maximize average or peak performance, it is very difficult to provide timing guarantees on such systems. In this work we present our ongoing work on GPUguard, a software technique that predictably arbitrates main memory usage in heterogeneous SoCs. A prototype implementation for the NVIDIA Tegra TX1 SoC shows that GPUguard is able to reduce the adverse effects of memory sharing, while retaining a high throughput on both the CPU and the accelerator.
2017
20th Design, Automation and Test in Europe, DATE 2017
SwissTech Convention Center, che
2017
318
321
Forsberg, Bjorn; Marongiu, Andrea; Benini, Luca
GPUguard: Towards supporting a predictable execution model for heterogeneous SoC / Forsberg, Bjorn; Marongiu, Andrea; Benini, Luca. - ELETTRONICO. - (2017), pp. 318-321. (Intervento presentato al convegno 20th Design, Automation and Test in Europe, DATE 2017 tenutosi a SwissTech Convention Center, che nel 2017) [10.23919/DATE.2017.7927008].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1171918
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