LLAMA: The low‐level abstraction for memory access. (8th March 2022)

• Record Type: Journal Article
• Authors: Gruber, Bernhard Manfred; Amadio, Guilherme; Blomer, Jakob; Matthes, Alexander; Widera, René; Bussmann, Michael
• Other Names: Chandrasekaran Sunita guestEditor.; Si Min guestEditor.; Zhai Jidong guestEditor.; Oden Lena guestEditor.
• Is Part Of: Software, practice & experience. Volume 53:Number 1(2023)
• Page Start: 115
• Page End: 141
• Publication Date: 2022-03-08
• Subjects: memory layout -- performance portability -- programming techniques -- software implementation; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.3077
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: The performance gap between CPU and memory widens continuously. Choosing the best memory layout for each hardware architecture is increasingly important as more and more programs become memory bound. For portable codes that run across heterogeneous hardware architectures, the choice of the memory layout for data structures is ideally decoupled from the rest of a program. This can be accomplished via a zero‐runtime‐overhead abstraction layer, underneath which memory layouts can be freely exchanged. We present the low‐level abstraction of memory access (LLAMA), a C++ library that provides such a data structure abstraction layer with example implementations for multidimensional arrays of nested, structured data. LLAMA provides fully C++ compliant methods for defining and switching custom memory layouts for user‐defined data types. The library is extensible with third‐party allocators. Providing two close‐to‐life examples, we show that the LLAMA‐generated array of structs and struct of arrays layouts produce identical code with the same performance characteristics as manually written data structures. Integrations into the SPEC CPU ® lbm benchmark and the particle‐in‐cell simulation PIConGPU demonstrate LLAMA's abilities in real‐world applications. LLAMA's layout‐aware copy routines can significantly speed up transfer and reshuffling of data between layouts compared with naive element‐wise copying. LLAMA provides a novel tool for the development of high‐performance C++ applications in a heterogeneous environment.