ROCm, Lingua Franca, C++, OpenCL and Python
The open-source ROCm stack offers multiple programming-language choices. The goal is to give you a range of tools to help solve the problem at hand. Here, we describe some of the options and how to choose among them.
HCC: Heterogeneous Compute Compiler
What is the Heterogeneous Compute (HC) API? It’s a C++ dialect with extensions to launch kernels and manage accelerator memory. It closely tracks the evolution of C++ and will incorporate parallelism and concurrency features as the C++ standard does. For example, HC includes early support for the C++17 Parallel STL. At the recent ISO C++ meetings in Kona and Jacksonville, the committee was excited about enabling the language to express all forms of parallelism, including multicore CPU, SIMD and GPU. We’ll be following these developments closely, and you’ll see HC move quickly to include standard C++ capabilities.
The Heterogeneous Compute Compiler (HCC) provides two important benefits:
Ease of development
- A full C++ API for managing devices, queues and events
- C++ data containers that provide type safety, multidimensional-array indexing and automatic data management
- C++ kernel-launch syntax using parallel_for_each plus C++11 lambda functions
- A single-source C++ programming environment—the host and source code can be in the same source file and use the same C++ language; templates and classes work naturally across the host/device boundary
- HCC generates both host and device code from the same compiler, so it benefits from a consistent view of the source code using the same Clang-based language parser
Full control over the machine
- Access AMD scratchpad memories (“LDS”)
- Fully control data movement, prefetch and discard
- Fully control asynchronous kernel launch and completion
- Get device-side dependency resolution for kernel and data commands (without host involvement)
- Obtain HSA agents, queues and signals for low-level control of the architecture using the HSA Runtime API
- Use direct-to-ISA compilation
When to Use HC
Use HC when you’re targeting the AMD ROCm platform: it delivers a single-source, easy-to-program C++ environment without compromising performance or control of the machine.
HIP: Heterogeneous-Computing Interface for Portability
What is Heterogeneous-Computing Interface for Portability (HIP)? It’s a C++ dialect designed to ease conversion of Cuda applications to portable C++ code. It provides a C-style API and a C++ kernel language. The C++ interface can use templates and classes across the host/kernel boundary.
The Hipify tool automates much of the conversion work by performing a source-to-source transformation from Cuda to HIP. HIP code can run on AMD hardware (through the HCC compiler) or Nvidia hardware (through the NVCC compiler) with no performance loss compared with the original Cuda code.
Programmers familiar with other GPGPU languages will find HIP very easy to learn and use. AMD platforms implement this language using the HC dialect described above, providing similar low-level control over the machine.
When to Use HIP
Use HIP when converting Cuda applications to portable C++ and for new projects that require portability between AMD and Nvidia. HIP provides a C++ development language and access to the best development tools on both platforms.
OpenCL™: Open Compute Language
What is OpenCL? It’s a framework for developing programs that can execute across a wide variety of heterogeneous platforms. AMD, Intel and Nvidia GPUs support version 1.2 of the specification, as do x86 CPUs and other devices (including FPGAs and DSPs). OpenCL provides a C run-time API and C99-based kernel language.
When to Use OpenCL
Use OpenCL when you have existing code in that language and when you need portability to multiple platforms and devices. It runs on Windows, Linux and Mac OS, as well as a wide variety of hardware platforms (described above).
Anaconda Python With Numba
What is Anaconda? It’s a modern open-source analytics platform powered by Python. Continuum Analytics, a ROCm platform partner, is the driving force behind it. Anaconda delivers high-performance capabilities including acceleration of HSA APUs, as well as ROCm-enabled discrete GPUs via Numba. It gives superpowers to the people who are changing the world.
Numba gives you the power to speed up your applications with high-performance functions written directly in Python. Through a few annotations, you can just-in-time compile array-oriented and math-heavy Python code to native machine instructions—offering performance similar to that of C, C++ and Fortran—without having to switch languages or Python interpreters.
Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, run time or statically (through the included Pycc tool). It supports Python compilation to run on either CPU or GPU hardware and is designed to integrate with Python scientific software stacks, such as NumPy.
When to Use Anaconda
Use Anaconda when you’re handling large-scale data-analytics, scientific and engineering problems that require you to manipulate large data arrays.
From a high-level perspective, ROCm delivers a rich set of tools that allow you to choose the best language for your application.
- HCC (Heterogeneous Compute Compiler) supports HC dialects
- HIP is a run-time library that layers on top of HCC (for AMD ROCm platforms; for Nvidia, it uses the NVCC compiler)
- The following will soon offer native compiler support for the GCN ISA:
- OpenCL 1.2+
- Anaconda (Python) with Numba
All are open-source projects, so you can employ a fully open stack from the language down to the metal. AMD is committed to providing an open ecosystem that gives developers the ability to choose; we are excited about innovating quickly using open source and about interacting closely with our developer community. More to come soon!