Contending memory in heterogeneous SoCs: Evolution in NVIDIA Tegra embedded platforms
| Autor: | Ignacio Sanudo Olmedo, Marco Solieri, Nicola Capodieci, Marko Bertogna, Roberto Cavicchioli |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Compute Accelerators
020203 distributed computing Tegra GP-GPU Memory Interference Real-Time business.industry Computer science Interference theory 02 engineering and technology Bottleneck 020202 computer hardware & architecture Software Shared memory Embedded system 0202 electrical engineering electronic engineering information engineering Central processing unit Scenario testing business Massively parallel Dram |
| Zdroj: | 2020 IEEE 26th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA) RTCSA |
| Popis: | Modern embedded platforms are known to be constrained by size, weight and power (SWaP) requirements. In such contexts, achieving the desired performance-per-watt target calls for increasing the number of processors rather than ramping up their voltage and frequency. Hence, generation after generation, modern heterogeneous System on Chips (SoC) present a higher number of cores within their CPU complexes as well as a wider variety of accelerators that leverages massively parallel compute architectures. Previous literature demonstrated that while increasing parallelism is theoretically optimal for improving on average performance, shared memory hierarchies (i.e. caches and system DRAM) act as a bottleneck by exposing the platform processors to severe contention on memory accesses, hence dramatically impacting performance and timing predictability. In this work we characterize how subsequent generations of embedded platforms from the NVIDIA Tegra family balanced the increasing parallelism of each platform's processors with the consequent higher potential on memory interference. We also present an open-source software for generating test scenarios aimed at measuring memory contention in highly heterogeneous SoCs. |
| Databáze: | OpenAIRE |
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