TransPimLib：面向存内计算系统的高效超越函数库
TransPimLib: A Library for Efficient Transcendental Functions on Processing-in-Memory Systems
AI总结：
针对通用存内计算系统难以执行超越函数等复杂操作的问题，提出TransPimLib库，基于CORDIC和LUT方法支持多种超越函数，并在UPMEM架构上实现与评估，有效提升了性能与准确性。
摘要：
存内计算（PIM）有望缓解现代计算系统中的数据移动瓶颈。然而，当前实际的PIM系统具有硬件比传统处理器（CPU、GPU）更受限的固有劣势，这是因为在存储器附近或内部构建处理元件存在困难且成本高昂。因此，通用PIM架构支持的指令集相当有限，难以执行复杂操作，如超越函数和其他难以计算的操作（例如平方根）。这些操作对于某些现代工作负载尤为重要，例如机器学习应用中的激活函数。为了在通用PIM系统中支持超越（及其他难以计算的）函数，我们提出了TransPimLib，这是一个为三角函数、双曲函数、指数运算、对数、平方根等提供基于CORDIC和基于LUT方法的库。我们为UPMEM PIM架构开发了TransPimLib的实现，并使用微基准测试和三个完整工作负载（Blackscholes、Sigmoid、Softmax），对TransPimLib的方法在性能和准确性方面进行了全面评估。我们在https://github.com/CMU-SAFARI/transpimlib开源了所有代码和数据集。
英文摘要：
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 hard-to-calculate) functions in general-purpose PIM systems, we present \emph{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~\url{https://github.com/CMU-SAFARI/transpimlib}.
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