  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 AoS
(Array of Structs) and SoA (Struct of Arrays) layouts produce identical code
with the same performance characteristics as manually written data structures.
Integrations into the SPEC CPU\textsuperscript{\textregistered} 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.
