Exploring iGPU Memory Interference Response to L2 Cache Locking

Authors Alfonso Mascareñas González , Jean-Baptiste Chaudron , Régine Leconte, Youcef Bouchebaba, David Doose

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Author Details

Alfonso Mascareñas González
  • ISAE-SUPAERO, Université de Toulouse, France
Jean-Baptiste Chaudron
  • ISAE-SUPAERO, Université de Toulouse, France
Régine Leconte
  • ISAE-SUPAERO, Université de Toulouse, France
Youcef Bouchebaba
  • ONERA, Université de Toulouse, France
David Doose
  • ONERA, Université de Toulouse, France

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Alfonso Mascareñas González, Jean-Baptiste Chaudron, Régine Leconte, Youcef Bouchebaba, and David Doose. Exploring iGPU Memory Interference Response to L2 Cache Locking. In 21th International Workshop on Worst-Case Execution Time Analysis (WCET 2023). Open Access Series in Informatics (OASIcs), Volume 114, pp. 3:1-3:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


The demand of parallel execution in real-time embedded applications has motivated the integration of GPUs as processing accelerators on SoCs (System-on-Chip) embedded architectures, often leading to CPU-iGPU architectures. In the safety-critical domain, it is paramount to ensure that the execution deadlines of critical tasks are not exceeded. To ease the analysis of this kind of tasks, we can make their worst-case execution time more predictable. One way to achieve this is by mitigating or controlling the memory interference generated by the concurrent execution of tasks through the application of a series of techniques (e.g., cache partitioning, bank partitioning, cache locking, bandwidth regulation). Originally, these were applied to CPUs, and more recently, to GPUs as well. In this work, we focus on the hardware-based L2 cache locking on iGPUs as memory interference mitigation mechanism. We are interested in evaluating its capacity for reducing the worst-case and the average-case execution time in different scenarios. Our measurement-based analysis has been carried out on the NVIDIA’s Jetson AGX Orin 64 GB MPSoC, making use of four representative benchmarks (data resetting, 2D convolution, 3D convolution and matrix upsampling).

Subject Classification

ACM Subject Classification
  • Computer systems organization → Embedded systems
  • Computer systems organization → Real-time systems
  • iGPU
  • cache locking
  • real-time
  • memory interference


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