Distributed memory programming and algorithms

Si è svolta a luglio la 15esima edizione della International Summer School on Advanced Computer Architecture and Compilation for High-Performance and Embedded Systems (ACACES 2019), organizzata da HiPEAC in collaborazione con TETRAMAX Innovation Action e Eurolab4HPC . Come per le edizioni precedenti, Reiss Romoli ha avuto un ruolo chiave nella organizzazione e gestione dell’evento.

A questa edizione hanno partecipato circa 220 ricercatori provenienti da molte delle università europee ed esperti del settore, con docenti provenienti da rinomate università americane e da industrie di punta del settore.

Durante la Summer School i partecipanti seguono 4 corsi, scelti tra i 12 del programma, oltre a un Keynote Speach e a un Invited Talk.

Nell’edizione 2019 di ACACES il prof. Scott Baden, della University of California, San Diego, ha tenuto un corso su:

Distributed memory programming and algorithms

Distributed memory computers provide bandwidth, processing, and memory scaling capabilities beyond what can be achieved via coherent shared memory. An important consideration in using distributed memory computers effectively is to keep communication costs low, since processing speeds are outpacing communication rates.

Two important models for programming distributed memory are message passing and RMA (Remote Memory Access). RMA comes in many forms, and benefits from global address space communication, that is generally supported by modern network hardware. RMA is employed in PGAS (Partitioned Global Address Space) models which adds global pointers, and optionally, remote procedure call.

These two capabilities play an important role in reducing communication costs, especially for fine grained and irregular communication patterns.

The course has covered message passing and PGAS programming via two libraries, respectively, MPI and UPC++.

The goal of the lectures has been to build a solid grounding in distributed memory programming and the performance trade-offs in efficient implementation.

Algorithmic studies have been presented, and hybrid hierarchical models have been also discussed, which compose distributed memory programming with programming at the node, e.g. multithreading.

The emphasis has been on maintaining low communication costs, as opposed to optimizing computational performance, which is another topic for study.

Scott Baden, Lawrence Berkeley National Laboratory, USA

Scott B. Baden is Group Lead of the Computer Languages and System Software Group in the Computational Research Division at Lawrence Berkeley National Laboratory, and Adjunct Professor of Computer Science and Engineering at the University of California, San Diego, where he was a faculty member for 27 years. He earned his Ph.D. from the University of California, Berkeley in 1987. His research interests are in high performance and scientific computation: domain specific translation, abstraction mechanisms, programming models, run times, and irregular problems.