An extension of C++ with memory-centric specifications for HPC to reduce memory footprints and streamline MPI development

TL;DR

This paper introduces a C++ language extension with attributes for memory optimization and MPI datatype modeling, aiming to reduce memory footprints and improve HPC development efficiency, demonstrated through smoothed particle hydrodynamics benchmarks.

Contribution

It proposes a standard-compatible language extension for memory-efficient data structures and MPI integration in C++, implemented in LLVM.

Findings

Significant memory reduction in data structures.

Improved performance in HPC benchmarks.

Enhanced developer productivity with new attributes.

Abstract

C++ leans towards a memory-inefficient storage of structs: The compiler inserts padding bits, while it is not able to exploit knowledge about the range of integers, enums or bitsets. Furthermore, the language provides no support for arbitrary floating-point precisions. We propose a language extension based upon attributes through which developers can guide the compiler what memory arrangements would be beneficial: Can multiple booleans or integers with limited range be squeezed into one bit field, do floating-point numbers hold fewer significant bits than in the IEEE standard, and is a programmer willing to trade attribute ordering guarantees for a more compact object representation? The extension offers the opportunity to fall back to normal alignment and native C++ floating point representations via plain C++ assignments, no dependencies upon external libraries are introduced, and the resulting code remains (syntactically) standard C++. As MPI remains the de-facto standard for distributed memory calculations in C++, we furthermore propose additional attributes which streamline the MPI datatype modelling in combination with our memory optimisation extensions. Our work implements the language annotations within LLVM and demonstrates their potential impact through smoothed particle hydrodynamics benchmarks. They uncover the potential gains in terms of performance and development productivity.