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Introduction

Selecting an appropriate parallel programming system is a key step in developing efficient, portable, and maintainable scientific applications. The E4S ecosystem provides a curated collection of programming models, frameworks, and libraries designed to help developers achieve high performance across diverse architectures including multicore CPUs, manycore GPUs, and emerging accelerators.

When choosing a programming system, it is important to consider factors such as portability, interoperability, runtime overhead, and developer productivity. For instance, a user running on a large GPU-based system might prioritize asynchronous execution and memory management models, while another working with mixed Fortran and C++ codebases might focus on compiler support and interoperability layers.

To help newcomers navigate these choices, the following sections define attributes that describe both general and specialized considerations for selecting a parallel programming system or library. These attributes can be used to formulate a detailed prompt for a chatbot or recommender tool to suggest suitable E4S products.

Example Prompt

I am developing a simulation code in C++ that needs to run efficiently on both NVIDIA and AMD GPUs. I prefer to use a performance-portable library that integrates with MPI for distributed memory and supports OpenMP on CPUs. I would like to minimize the need for vendor-specific code changes and ensure good support for asynchronous execution and profiling tools.

Broadly Meaningful Attributes for Programming Systems

Attribute Description
Target architectures The types of architectures supported, such as CPUs, GPUs, or multi-accelerator systems.
Programming languages The primary languages supported (C, C++, Fortran, Python).
Portability model The extent to which the programming system allows running on multiple vendors’ hardware with minimal changes.
Performance portability How effectively the framework can deliver performance across architectures without rewriting kernels.
Abstraction level The level of abstraction provided, from low-level APIs to high-level frameworks.
Integration with MPI Whether the system supports or interoperates with MPI for inter-node communication.
Memory model How data movement and memory management are handled between host and device.
Asynchronous execution Support for task-based or asynchronous parallelism.
Debugging and profiling support Availability of integrated tools or external support for performance analysis.
Community and documentation The strength of community support, user guides, and active development.
Licensing and openness Availability under open-source licenses and alignment with E4S standards.

Attributes for Specific Situations

Intra-Node Systems

Attribute Description
Shared memory model Whether the system supports shared memory parallelism (e.g., OpenMP, CUDA threads).
Task scheduling Support for dynamic task scheduling or work-stealing mechanisms.
Thread affinity and control Ability to control thread placement and binding for NUMA systems.
Vectorization support Level of compiler or library-assisted vectorization.
GPU kernel execution model Control and flexibility in defining and launching GPU kernels.
Device memory hierarchy awareness Capability to utilize L1/L2 caches and shared memory efficiently.
Compiler dependencies Required or preferred compiler infrastructure (LLVM, GCC, NVHPC, etc.).

Inter-Node Systems

Attribute Description
Communication model Message passing or PGAS-based communication model.
Latency and bandwidth optimization Support for minimizing communication overhead and optimizing collective operations.
Fault tolerance Capabilities for checkpoint/restart or resilience in distributed environments.
Overlap of communication and computation Ability to perform asynchronous communications with concurrent computation.
Network portability Compatibility with InfiniBand, Slingshot, Ethernet, and other HPC interconnects.
Integration with resource managers Interoperability with job schedulers such as SLURM or Flux.
Hybrid parallelism Support for combining distributed and shared-memory parallel models (MPI + threads).
Performance analysis hooks Interfaces to collect inter-node communication traces and performance metrics.

Language-Supported Systems (Modern Fortran and C++ via LLVM Compilers)

Attribute Description
Standard support level Compliance with Fortran 2008+, C++17+, or newer standards.
Compiler-based directives Availability of OpenMP, OpenACC, or SYCL support in the compiler.
Unified memory model Simplified access to device and host memory through unified memory.
Template and metaprogramming features Ability to define portable kernels using C++ templates or Fortran coarrays.
Integration with performance libraries Direct compatibility with BLAS, LAPACK, KokkosKernels, or other math libraries.
Compiler optimization maturity Level of maturity and stability of compiler optimizations for each architecture.
Language interoperability Ease of calling C/C++ libraries from Fortran or vice versa.
Toolchain integration Integration with LLVM-based analysis, debugging, and profiling tools.
Vendor backend support Availability of target backends (NVIDIA, AMD, Intel, ARM) through the compiler toolchain.