Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162977
Title: The Bitlet model: a parameterized analytical model to compare PIM and CPU systems
Authors: Ronen, Ronny
Eliahu, Adi
Leitersdorf, Orian
Peled, Natan
Korgaonkar, Kunal
Chattopadhyay, Anupam
Perach, Ben
Kvatinsky, Shahar
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Ronen, R., Eliahu, A., Leitersdorf, O., Peled, N., Korgaonkar, K., Chattopadhyay, A., Perach, B. & Kvatinsky, S. (2022). The Bitlet model: a parameterized analytical model to compare PIM and CPU systems. ACM Journal On Emerging Technologies in Computing Systems, 18(2), 1-29. https://dx.doi.org/10.1145/3465371
Journal: ACM Journal on Emerging Technologies in Computing Systems 
Abstract: Currently, data-intensive applications are gaining popularity. Together with this trend, processing-in-memory (PIM)-based systems are being given more attention and have become more relevant. This article describes an analytical modeling tool called Bitlet that can be used in a parameterized fashion to estimate the performance and power/energy of a PIM-based system and, thereby, assess the affinity of workloads for PIM as opposed to traditional computing. The tool uncovers interesting trade-offs between, mainly, the PIM computation complexity (cycles required to perform a computation through PIM), the amount of memory used for PIM, the system memory bandwidth, and the data transfer size. Despite its simplicity, the model reveals new insights when applied to real-life examples. The model is demonstrated for several synthetic examples and then applied to explore the influence of different parameters on two systems - IMAGING and FloatPIM. Based on the demonstrations, insights about PIM and its combination with a CPU are provided.
URI: https://hdl.handle.net/10356/162977
ISSN: 1550-4832
DOI: 10.1145/3465371
Rights: © 2022 Association for Computing Machinery. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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