Pawel K. Radtke, Cristian G. Barrera-Hinojosa, Mladen Ivkovic, Tobias Weinzierl
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引用次数: 0
摘要
C++ 编程语言及其同类语言倾向于低内存效率的结构体存储:编译器会在结构体中插入辅助位,使单个属性与字节对齐,并添加额外的字节使属性与缓存行对齐,而无法利用整数、枚举或比特集的范围知识来减少内存占用。此外,该语言既不支持通过 MPI 进行数据交换,也不支持任意浮点精度格式。如果开发人员需要在只交换极少量数据的结构体上使用低内存占用和 MPI 数据类型,就必须手动操作数据和编写 MPI 数据类型。我们提出了一种基于 C++ 属性的 C++ 语言扩展,开发人员可以通过它指导编译器如何安排内存:是否可以将多个布尔值挤入一个比特字段,浮点数是否比 IEEE 标准中的有效位数更少,代码是否需要为某些属性子集提供用户定义的 MPI 数据类型?我们的工作在 LLVM 中实现了语言注释,并通过平滑粒子流体力学(SPH)基准测试证明了语言注释对运行时间和内存占用的潜在影响。我们的工作在 LLVM 中实现了语言注解,并通过平滑粒子流体力学(SPH)基准测试证明了其对运行时间和内存占用的潜在影响。
An extension of C++ with memory-centric specifications for HPC to reduce memory footprints and streamline MPI development
The C++ programming language and its cousins lean towards a
memory-inefficient storage of structs: The compiler inserts helper bits into
the struct such that individual attributes align with bytes, and it adds
additional bytes aligning attributes with cache lines, while it is not able to
exploit knowledge about the range of integers, enums or bitsets to bring the
memory footprint down. Furthermore, the language provides neither support for
data exchange via MPI nor for arbitrary floating-point precision formats. If
developers need to have a low memory footprint and MPI datatypes over structs
which exchange only minimal data, they have to manipulate the data and to write
MPI datatypes manually. We propose a C++ language extension based upon C++
attributes through which developers can guide the compiler what memory
arrangements would be beneficial: Can multiple booleans be squeezed into one
bit field, do floats hold fewer significant bits than in the IEEE standard, or
does the code require a user-defined MPI datatype for certain subsets of
attributes? The extension offers the opportunity to fall back to normal
alignment and padding rules via plain C++ assignments, no dependencies upon
external libraries are introduced, and the resulting code remains standard C++.
Our work implements the language annotations within LLVM and demonstrates their
potential impact, both upon the runtime and the memory footprint, through
smoothed particle hydrodynamics (SPH) benchmarks. They uncover the potential
gains in terms of performance and development productivity.