TransPimLib: Efficient Transcendental Functions for Processing-in-Memory Systems

Processing-in-memory (PIM) promises to alleviate the data movement bottleneck in modern computing systems. However, current real-world PIM systems have the inherent disadvantage that their hardware is more constrained than in conventional processors (CPU, GPU), due to the difficulty and cost of buil...

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Bibliographic Details
Published in2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) pp. 235 - 247
Main Authors Item, Maurus, Oliveira, Geraldo F., Gomez-Luna, Juan, Sadrosadati, Mohammad, Guo, Yuxin, Mutlu, Onur
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2023
Subjects
activation functions
Computer architecture
Graphics processing units
Instruction sets
Libraries
Machine learning
processing-in-memory
processing-near-memory
Software
transcendental functions
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DOI10.1109/ISPASS57527.2023.00031

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Summary:Processing-in-memory (PIM) promises to alleviate the data movement bottleneck in modern computing systems. However, current real-world PIM systems have the inherent disadvantage that their hardware is more constrained than in conventional processors (CPU, GPU), due to the difficulty and cost of building processing elements near or inside the memory. As a result, general-purpose PIM architectures support fairly limited instruction sets and struggle to execute complex operations such as transcendental functions and other hard-to-calculate operations (e.g., square root). These operations are particularly important for some modern workloads, e.g., activation functions in machine learning applications. In order to provide support for transcendental (and other hardto-calculate) functions in general-purpose PIM systems, we present TransPimLib, a library that provides CORDIC-based and LUT-based methods for trigonometric functions, hyperbolic functions, exponentiation, logarithm, square root, etc. We develop an implementation of TransPimLib for the UPMEM PIM architecture and perform a thorough evaluation of TransPimLib's methods in terms of performance and accuracy, using microbenchmarks and three full workloads (Blackscholes, Sigmoid, Softmax). We open-source all our code and datasets at https://github.com/CMU-SAFARI/transpimlib.
DOI:10.1109/ISPASS57527.2023.00031