Intrinsic function

In computer software, in compiler theory, an intrinsic function (or built-in function) is a function (subroutine) available for use in a given programming language whose implementation is handled specially by the compiler. Typically, it may substitute a sequence of automatically generated instructions for the original function call, similar to an inline function. Unlike an inline function, the compiler has an intimate knowledge of an intrinsic function and can thus better integrate and optimize it for a given situation. Compilers that implement intrinsic functions generally enable them only when a program requests optimization, otherwise falling back to a default implementation provided by the language runtime system (environment). Intrinsic functions are often used to explicitly implement vectorization and parallelization in languages which do not address such constructs. Some application programming interfaces (API), for example, AltiVec and OpenMP, use intrinsic functions to declare, respectively, vectorizable and multiprocessing-aware operations during compiling. The compiler parses the intrinsic functions and converts them into vector math or multiprocessing object code appropriate for the target platform. Some intrinsics are used to provide additional constraints to the optimizer, such as values a variable cannot assume. Compilers for C and C++, of Microsoft, Intel, and the GNU Compiler Collection (GCC) implement intrinsics that map directly to the x86 single instruction, multiple data (SIMD) instructions (MMX, Streaming SIMD Extensions (SSE), SSE2, SSE3, SSSE3, SSE4, AVX, AVX2, AVX512, FMA, ...). The Microsoft Visual C++ compiler of Microsoft Visual Studio does not support inline assembly for x86-64. To compensate for this, new intrinsics have been added that map to standard assembly instructions that are not normally accessible through C/C++, e.g., bit scan. Some C and C++ compilers provide non-portable platform-specific intrinsics.

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

Aurélien François Gilbert Bloch

Today's continued increase in demand for processing power, despite the slowdown of Moore's law, has led to an increase in processor count, which has resulted in energy consumption and distribution problems. To address this, there is a growing trend toward ...

EPFL2023

Dynamic SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis

Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch

Developing and fine-tuning software programs for heterogeneous hardware such as CPU/GPU processing platforms comprise a highly complex endeavor that demands considerable time and effort of software engineers and requires evaluating various fundamental comp ...

MDPI2022

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

Dynamic SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis

Holistic, Efficient, and Real-time Cleaning of Heterogeneous Data

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

Holistic, Efficient, and Real-time Cleaning of Heterogeneous Data

Dynamic SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis