Forma: a DSL for image processing applications to target GPUs and multi-core CPUs

Abstract

As architectures evolve, optimization techniques to obtain good performance evolve as well. Using low-level programming languages like C/C++ typically results in architecture-specific optimization techniques getting entangled with the application specification. In such situations, moving from one target architecture to another usually requires a reimplementation of the entire application. Further, several compiler transformations are rendered ineffective due to implementation choices. Domain-Specific Languages (DSL) tackle both these issues by allowing developers to specify the computation at a high level, allowing the compiler to handle many tedious and error-prone tasks, while generating efficient code for multiple target architectures at the same time. Here we present Forma, a DSL for image processing applications that targets both CPUs and GPUs. The language provides syntax to express several operations like stencils, sampling, etc. which are commonly used in this domain. These can be chained together to specify complex pipelines in a concise manner. The Forma compiler is in charge of tedious tasks like memory management, data transfers from host to device, handling boundary conditions, etc. The high-level description allows the compiler to generate efficient code through use of compile-time analysis and by taking advantage of hardware resources, like texture memory on GPUs. The ease with which complex pipelines can be specified in Forma is demonstrated through several examples. The efficiency of the generated code is evaluated through comparison with a state-of-the-art DSL that targets the same domain, Halide. Our experimental result show that using Forma allows developers to obtain comparable performance on both CPU and GPU with lesser programmer effort. We also show how Forma could be easily integrated with widely used productivity tools like Python and OpenCV. Such an integration would allow users of such tools to develop efficient implementations easily.