Heterogeneous Memory Management for Graph Applications

Enviado por hugo.chaves em qui, 16/02/2023 - 23:11

Nome completo do aluno

Diêgo Braga Monteiro de Moura

Título do trabalho

Resumo do trabalho

The demand for memory has increased due to various applications such as big data, machine learning, social networks, and streaming analytics. DRAM technology is facing issues with scalability, energy, and costs, despite its low latency advantage. Non-Volatile Memory (NVM) is an alternative to DRAM scalability issues. Although NVM has high density, low cost, and low energy, its high latency prevents it from replacing DRAM. Heterogeneous memories like DRAM+NVM are likely to become common, and efficient data placement is an important research question. This PhD dissertation focuses on managing data placement for graph analytics applications. It studies machine learning models to predict application performance in a context with multiple applications and explores data mapping choices at the object or chunk level for a single application. The dissertation contributes to offline identification of performance-influencing object attributes, implementation of an offline scheduler at the chunk level, and implementation of an online scheduler at the chunk level.

Orientador

Vinicius Tavares Petrucci

Co-orientador

Daniel Mossé

Membro Titular Externo 1 (com afiliação)

Esbel Tomás Valero Orellana (Univerisdade Estadual de Santa Cruz)

Link para o curriculum lattes

http://lattes.cnpq.br/8384020879567133

Membro Titular Externo 2 (com afiliação)

Paul M. Carpenter (Barcelona Supercomputing Center)

Link para o curriculum lattes

https://scholar.google.es/citations?user=V9UomEgAAAAJ