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UID:submissions.supercomputing.org_SC24_sess533_post170@linklings.com
SUMMARY:Exploring Fine-Grained Memory Analysis for PIM Offloading
DESCRIPTION:Thomas Papka (Loyola University, Chicago) and Nanda Velugoti a
 nd Kyle Hale (Illinois Institute of Technology)\n\nIn modern computing, a 
 challenge is the data bottleneck between the CPU and RAM. This issue arise
 s because the CPU can process data faster than it can be accessed from RAM
 ; this is worsened by the fact that large amounts of RAM are less accessib
 le than a powerful CPU. Furthermore, RAM’s high cost creates a need for a 
 cost-effective solution. Processing in Memory (PIM) offers a potential rem
 edy by reducing data movement, thus alleviating bottlenecks. To optimize t
 he use of this new hardware, developers need to identify when to offload t
 heir programs to a PIM device. To address this need, we have developed a s
 olution that enables developers to run Python programs through our pipelin
 e, highlighting the memory-intensive parts of their code.\n\nRegistration 
 Category: Tech Program Reg Pass, Exhibits Reg Pass\n\nSession Chairs: Ayes
 ha Afzal (Friedrich-Alexander University, Erlangen-Nuremberg; Erlangen Nat
 ional High Performance Computing Center); Sally Ellingson (University of K
 entucky); and Alan Sussman (University of Maryland)\n\n
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