Hybrid Fortran

"Hybrid Fortran is ..

• .. a directive based extension for the Fortran language.
• .. a way for you to keep writing your Fortran code like you're used to - only now with GPGPU support.
• .. a preprocessor for your code - its input are Fortran files (with Hybrid Fortran extensions), its output is CUDA Fortran or OpenMP Fortran code (or whatever else you'd like to have as a backend).
• .. a build system that handles building two separate versions (CPU / GPU) of your codebase automatically, including all the preprocessing.
• .. a test system that handles verification of your outputs automatically after setup.
• .. a framework for you to build your own parallel code implementations (OpenCL, ARM, FPGA, Hamster Wheel.. as long as it has some parallel Fortran support you're good) while keeping the same source files.

Hybrid Fortran has been successfully used for porting the Physical Core of Japan's national next generation weather prediction model to GPGPU. We're currently planning to port the internationally used Open Source weather model WRF to Hybrid Fortran as well."

https://github.com/muellermichel/Hybrid-Fortran

Hybrid Fortran: High Productivity GPU Porting Framework Applied to Japanese Weather Prediction Model - https://hgpu.org/?p=17723

In this work we use the GPU porting task for the operative Japanese weather prediction model "ASUCA" as an opportunity to examine productivity issues with OpenACC when applied to structured grid problems. We then propose "Hybrid Fortran", an approach that combines the advantages of directive based methods (no rewrite of existing code necessary) with that of stencil DSLs (memory layout is abstracted). This gives the ability to define multiple parallelizations with different granularities in the same code. Without compromising on performance, this approach enables a major reduction in the code changes required to achieve a hybrid GPU/CPU parallelization – as demonstrated with our ASUCA implementation using Hybrid Fortran.

Refactorf4acc

An Automated Fortran Code Refactoring Tool to Facilitate Acceleration of Numerical Simulations.

https://github.com/wimvanderbauwhede/RefactorF4Acc 

Domain-Specific Acceleration and Auto-Parallelization of Legacy Scientific Code in FORTRAN 77 using Source-to-Source Compilation - https://hgpu.org/?p=17771

Massively parallel accelerators such as GPGPUs, manycores and FPGAs represent a powerful and affordable tool for scientists who look to speed up simulations of complex systems. However, porting code to such devices requires a detailed understanding of heterogeneous programming tools and effective strategies for parallelization. In this paper we present a source to source compilation approach with whole-program analysis to automatically transform single-threaded FORTRAN 77 legacy code into OpenCL-accelerated programs with parallelized kernels. The main contributions of our work are: (1) whole-source refactoring to allow any subroutine in the code to be offloaded to an accelerator. (2) Minimization of the data transfer between the host and the accelerator by eliminating redundant transfers. (3) Pragmatic auto-parallelization of the code to be offloaded to the accelerator by identification of parallelizable maps and reductions. We have validated the code transformation performance of the compiler on the NIST FORTRAN 78 test suite and several real-world codes: the Large Eddy Simulator for Urban Flows, a high-resolution turbulent flow model; the shallow water component of the ocean model Gmodel; the Linear Baroclinic Model, an atmospheric climate model and Flexpart-WRF, a particle dispersion simulator. The automatic parallelization component has been tested on as 2-D Shallow Water model (2DSW) and on the Large Eddy Simulator for Urban Flows (UFLES) and produces a complete OpenCL-enabled code base. The fully OpenCL-accelerated versions of the 2DSW and the UFLES are resp. 9x and 20x faster on GPU than the original code on CPU, in both cases this is the same performance as manually ported code.

AClang

"ACLang is an open source LLVM Clang based compiler that implements the OpenMP Accelerator Model. It adds a new runtime library to LLVM/CLang that supports OpenMP offloading to accelerators like GPUs. Kernel functions are extracted from OpenMP annotated regions and are dispatched as OpenCL or SPIR code to be loaded and compiled by OpenCL drivers before being executed by the device. AClang leverages on the ISL implementation of the polyhedral model to implement a multilevel tiling optimization on the extracted kernels. AClang also provides a vectorization pass developed specifically to exploit the vector instructions available in OpenCL. This whole process is transparent and does not require any programmer intervention."

https://omp2ocl.github.io/aclang/

Compiling and Optimizing OpenMP 4.X Programs to OpenCL and SPIR - https://hgpu.org/?p=17798

Given their massively parallel computing capabilities heterogeneous architectures comprised of CPUs and accelerators have been increasingly used to speed-up scientific and engineering applications. Nevertheless, programming such architectures is a challenging task for most non-expert programmers as typical accelerator programming languages (e.g. CUDA and OpenCL) demand a thoroughly understanding of the underlying hardware to enable an effective application speed-up. To achieve that, programmers are usually required to significantly change and adapt program structures and algorithms, thus impacting both performance and productivity. A simpler alternative is to use high-level directive-based programming models like OpenACC and OpenMP. These models allow programmers to insert both directives and runtime calls into existing source code, thus providing hints to the compiler and runtime to perform certain transformations and optimizations on the annotated code regions. In this paper, we present ACLang, an open-source LLVM/Clang compiler framework (http://www.aclang.org) that implements the recently released OpenMP 4.X Accelerator Programming Model. ACLang automatically converts OpenMP 4.X annotated program regions into OpenCL/SPIR kernels, while providing a set of polyhedral based optimizations like tiling and vectorization. OpenCL kernels resulting from ACLang can be executed on any OpenCL/SPIR compatible acceleration device, not only GPUs, but also FPGA accelerators like those found in the Intel HARP architecture. To the best of our knowledge and at the time this paper was written, this is the first LLVM/Clang implementation of the OpenMP 4.X Accelerator Model that provides a source-totarget OpenCL conversion.

CUDAnative.jl

"Julia support for native CUDA programming.

 GPUs and other accelerators are popular devices for accelerating compute-intensive, parallelizable applications. However, programming these devices is a difficult task. Writing efficient device code is challenging, and is typically done in a low-level programming language. High-level languages are rarely supported, or do not integrate with the rest of the high-level language ecosystem. To overcome this, we propose compiler infrastructure to efficiently add support for new hardware or environments to an existing programming language. We evaluate our approach by adding support for NVIDIA GPUs to the Julia programming language. By integrating with the existing compiler, we significantly lower the cost to implement and maintain the new compiler, and facilitate reuse of existing application code. Moreover, use of the high-level Julia programming language enables new and dynamic approaches for GPU programming. This greatly improves programmer productivity, while maintaining application performance similar to that of the official NVIDIA CUDA toolkit.
 
https://github.com/JuliaGPU/CUDAnative.jl 

https://hgpu.org/?p=17878 

LIFT

"Lift is a novel approach to achieving performance portability on parallel accelerators. Lift combines a high-level functional data parallel language with a system of rewrite rules which encode algorithmic and hardware-specific optimisation choices. Applications written in Lift are able to take advantage of GPUs (and in the future other accelerators), transparently from the user.

"Lift is a high-level domain-specific language and source-to-source compiler, targeting single system as well as distributed heterogeneous hardware. Initially targeting image processing algorithms, our framework now also handles general stencil-based operations. It resembles OpenCL, but abstracts away performance optimization details which instead are handled by our source-to-source compiler. Machine learning-based auto-tuning is used to determine which optimizations to apply. For the distributed case, by measuring performance counters on a small input on one device, previously trained performance models are used to predict the throughput of the application on multiple different devices, making it possible to balance the load evenly. Models for the communication overhead are created in a similar fashion and used to predict the optimal number of nodes to use." 


http://www.lift-project.org/

https://github.com/lift-project/lift

https://hgpu.org/?p=17926

ImageCL

"ImageCL is a simplified version of OpenCL, specifically aimed at image processing. A source-to-source translator can translate a single ImageCL program into multiple different OpenCL versions, each version with different optimizations applied. An auto-tuner can then be used to pick the best OpenCL version for a given device. This makes it possible to write a program once, and run it with high performance on a range of different devives."

https://github.com/acelster/ImageCL

https://hgpu.org/?p=17926

ratfor90

“Ratfor” is a dialect of Fortran that is more concise than raw Fortran. The newest Ratfor “compiler”, ratfor90 is a simple preprocesor program written in Perl that inputs an attractive Fortran-like dialect and outputs Fortran90. Mainly, the preprocessor produces Fortran statements like ” end do ”, “end if”, “end program”, and “end module”, from the Ratfor ”}”. Ratfor source is about 25-30% smaller than the equivalent Fortran making it equivalently more readable.

Bare-bones Fortran is our most universal computer language for computational physics. For general programming, however, it has been surpassed by C. Ratfor is Fortran with C-like syntax. Ratfor was invented by the people who invented C. After inventing C, they realized that they had made a mistake (too many semicolons) and they fixed it in Ratfor, although it was too late for C. Otherwise, Ratfor uses C-like syntax, the syntax that is also found in the popular languages C++ and Java .

At SEP we supplemented Ratfor77 by preprocessors to give Fortran77 the ability to allocate memory on the fly. These abilities are built into Fortran90 and are seamlessly included in Ratfor90. To take advantage of Fortran90's new features while maintaining the concise coding style provided by Ratfor required us to write a new Ratfor preprocessor, Ratfor90, which produces Fortran90, rather than Fortran77 code."

http://sepwww.stanford.edu/doku.php?id=sep:software:ratfor90