We study the performance behaviour of a seismic simulation using the ExaHyPE engine with a specific focus on memory characteristics and energy needs. ExaHyPE combines dynamically adaptive mesh refinement (AMR) with ADER-DG. It is parallelized using tasks, and it is cache efficient. AMR plus ADER-DG yields a task graph which is highly dynamic in nature and comprises both arithmetically expensive tasks and tasks which challenge the memory’s latency. The expensive tasks and thus the whole code benefit from AVX vectorization, although we suffer from memory access bursts. A frequency reduction of the chip improves the code’s energy-to-solution. Yet, it does not mitigate burst effects. The bursts’ latency penalty becomes worse once we add Intel Optane technology, increase the core count significantly or make individual, computationally heavy tasks fall out of close caches. Thread overbooking to hide away these latency penalties becomes contra-productive with noninclusive caches as it destroys the cache and vectorization character. In cases where memoryintense and computationally expensive tasks overlap, ExaHyPE’s cache-oblivious implementation nevertheless can exploit deep, noninclusive, heterogeneous memory effectively, as main memory misses arise infrequently and slow down only few cores. We thus propose that upcoming supercomputing simulation codes with dynamic, inhomogeneous task graphs are actively supported by thread runtimes in intermixing tasks of different compute character, and we propose that future hardware actively allows codes to downclock the cores running particular task types.

Studies on the Energy and Deep Memory Behaviour of a Cache-oblivious, Task-based Hyperbolic PDE Solver / E Charrier, Dominic; Hazelwood, Benjamin; Tutlyaeva, Ekaterina; Bader, Michael; Dumbser, Michael; Kudryavtsev, Andrey; Moskovsky, Alexander; Weinzierl, Tobias. - In: INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS. - ISSN 1094-3420. - 2019, 33 (5):5(2019), pp. 973-986. [10.1177/1094342019842645]

Studies on the Energy and Deep Memory Behaviour of a Cache-oblivious, Task-based Hyperbolic PDE Solver

Michael Dumbser;
2019-01-01

Abstract

We study the performance behaviour of a seismic simulation using the ExaHyPE engine with a specific focus on memory characteristics and energy needs. ExaHyPE combines dynamically adaptive mesh refinement (AMR) with ADER-DG. It is parallelized using tasks, and it is cache efficient. AMR plus ADER-DG yields a task graph which is highly dynamic in nature and comprises both arithmetically expensive tasks and tasks which challenge the memory’s latency. The expensive tasks and thus the whole code benefit from AVX vectorization, although we suffer from memory access bursts. A frequency reduction of the chip improves the code’s energy-to-solution. Yet, it does not mitigate burst effects. The bursts’ latency penalty becomes worse once we add Intel Optane technology, increase the core count significantly or make individual, computationally heavy tasks fall out of close caches. Thread overbooking to hide away these latency penalties becomes contra-productive with noninclusive caches as it destroys the cache and vectorization character. In cases where memoryintense and computationally expensive tasks overlap, ExaHyPE’s cache-oblivious implementation nevertheless can exploit deep, noninclusive, heterogeneous memory effectively, as main memory misses arise infrequently and slow down only few cores. We thus propose that upcoming supercomputing simulation codes with dynamic, inhomogeneous task graphs are actively supported by thread runtimes in intermixing tasks of different compute character, and we propose that future hardware actively allows codes to downclock the cores running particular task types.
2019
5
E Charrier, Dominic; Hazelwood, Benjamin; Tutlyaeva, Ekaterina; Bader, Michael; Dumbser, Michael; Kudryavtsev, Andrey; Moskovsky, Alexander; Weinzierl, Tobias
Studies on the Energy and Deep Memory Behaviour of a Cache-oblivious, Task-based Hyperbolic PDE Solver / E Charrier, Dominic; Hazelwood, Benjamin; Tutlyaeva, Ekaterina; Bader, Michael; Dumbser, Michael; Kudryavtsev, Andrey; Moskovsky, Alexander; Weinzierl, Tobias. - In: INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS. - ISSN 1094-3420. - 2019, 33 (5):5(2019), pp. 973-986. [10.1177/1094342019842645]
File in questo prodotto:
File Dimensione Formato  
ExaHyPE-DeepMemory.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 736.58 kB
Formato Adobe PDF
736.58 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/360645
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 10
  • ???jsp.display-item.citation.isi??? ND
social impact