Fortran at LBNL

Fortran 2023, with its "do concurrent" and coarray parallel programming features, displaces many uses of extra-language parallel programming models such as MPI, OpenMP, and OpenACC. The Cray, Intel, LFortran, LLVM, and NVIDIA compilers automatically parallelize do concurrent in shared memory. The Cray, Intel, and GNU compilers support coarrays in shared- and distributed-memory, while the NAG compiler supports coarrays in shared memory. Thus, language-based parallelism is emerging as a portable alternative to MPI+X.

This talk will present experiences with automatic "do concurrent" parallelization in the deep learning library Inference-Engine and coarray communication in the Intermediate Complexity Atmospheric Research (ICAR), respectively.

PAW-ATM24

Fortran 2023 natively supports single-program, multiple-data parallel programming with a partitioned global address space and collective subroutines, synchronization, atomics, locks, and more. Each of the four actively developed compilers that support Fortran’s parallel features uses its own parallel runtime library. The Parallel Runtime Interface for Fortran (PRIF) proposes to liberate compiler development from reliance on a single runtime and empower runtime developers to support more than one compiler. PRIF also aims to broaden the community of runtime developers to include the Fortran compiler’s users: Fortran programmers. PRIF does so by specifying the interface in Fortran, which makes it attractive to write the parallel runtime library in Fortran. Additionally, PRIF has been designed to be portable across both shared and distributed memory, varying architectures, as well as different operating systems. In this talk, I will describe the motivation behind the development of PRIF, describe the design of the interface itself and the benefits of adopting it. I will also provide a brief status report on the first PRIF implementation: Caffeine.

PASC'24 site

In 1951, Harlem Renaissance poet Langston Hughes asked this talk's titular question at the outset of a poem entitled "Harlem." Six years later, IBM mathematician John Backus developed Fortran, the world's first widely used high-level programming language. Backus went on to explore functional programming and to highlight the functional style in his Turing Award lecture in 1977, a year that also demarcates what one might consider the end of the classical era of Fortran. This talk will demonstrate how modern Fortran began to deliver on Backus's functional programming dream, starting with pure procedures in the 1995 standard. The talk will further demonstrate how this style culminated in a powerful and flexible facility for expressing independent iterations via the "do concurrent" construct, which the Fortran standard committee included in Fortran 2008 with the intention to facilitate automatic Graphics Processing Unit (GPU) programming. Fortran 2008 was published in 2010, but it took another decade for compilers to deliver on the promise of automatic GPU offloading. This talk will detail the trials and tribulations of Berkeley Lab's Fortran team in chasing the automatic offloading dream in our Inference-Engine deep learning library and Matcha high-performance computing (HPC) application.

A majority of HPC system users utilize scripting languages such as Python to prototype their computations, coordinate their large executions, and analyze the data resulting from their computations. Python is great for these many uses, but it frequently falls short when significantly scaling up the amount of data and computation, as required to fully leverage HPC system resources. In this tutorial, we show how example computations such as heat diffusion, k-mer counting, file processing, and distributed maps can be written to efficiently leverage distributed computing resources in the Chapel, UPC++, and Fortran parallel programming models.

The tutorial is targeted for users with little-to-no parallel programming experience, but everyone is welcome. A partial differential equation example will be demonstrated in all three programming models. That example and others will be provided to attendees in a virtual environment. Attendees will be shown how to compile and run these programming examples, and the virtual environment will remain available to attendees throughout the conference, along with Slack-based interactive tech support.

Come join us to learn about some productive and performant parallel programming models!

SC23 event page

A majority of HPC system users utilize scripting languages such as Python to prototype their computations, coordinate their large executions, and analyze the data resulting from their computations. Python is great for these many uses, but it frequently falls short when significantly scaling up the amount of data and computation, as required to fully leverage HPC system resources. In this tutorial, we show how example computations such as heat diffusion, k-mer counting, file processing, and distributed maps can be written to efficiently leverage distributed computing resources in the Chapel, UPC++, and Fortran parallel programming models. This tutorial should be accessible to users with little-to-no parallel programming experience, and everyone is welcome. A partial differential equation example will be demonstrated in all three programming models along with performance and scaling results on big machines. That example and others will be provided in a cloud instance and Docker container. Attendees will be shown how to compile and run these programming examples, and provided opportunities to experiment with different parameters and code alternatives while being able to ask questions and share their own observations. Come join us to learn about some productive and performant parallel programming models!

Secondary tutorial sites by event sponsors:

• Oak Ridge Leadership Computing Facility (OLCF)
• National Energy Research Scientific Computing Center (NERSC)
• Exascale Computing Project (ECP)

The Computer Languages and Systems Software (CLaSS) Group at Berkeley Lab researches and develops programming models, languages, libraries, and applications for parallel and quantum computing. The open-source software under development in CLaSS includes the GASNet-EX networking middleware, the UPC++ partitioned global address space (PGAS) template library, the Berkeley Quantum Synthesis Toolkit (BQSKit), and the MetaHipMer metagenome assembler. This talk will start with an overview of CLaSS software and the software sustainability practices commonly employed across the group. The talk will then dive more deeply into the our burgeoning contributions to the ecosystem supporting modern Fortran, including our test development for the LLVM Flang Fortran compiler. This presentation will demonstrate how agile software development techniques are helping to ensure robust front-end support for standard Fortran 2018 parallel programming features. The talk will also present several key insights that inspired our design and development of the CoArray Fortran Framework of Efficient Interfaces to Network Environments (Caffeine) parallel runtime library, emphasizing the design choices that help to ensure sustainability. Lastly, the talk will demonstrate the productivity benefits associated with the first Caffeine application in Motility Analysis of T-Cell Histories in Activation (Matcha).

SIAM Session