OpenMP for reconfigurable heterogenous architectures

High-Performance Computing (HPC) is an important component of Europe's capacity for innovation and it is also seen as a building block of the digitization of the European industry. Reconfigurable technologies such as Field Programmable Gate Array (FPGA) modules are gaining in importance due to their energy efficiency, performance, and flexibility.
There is also a trend towards heterogeneous systems with accelerators utilizing FPGAs. The great flexibility of FPGAs allows for a large class of HPC applications to be realized with FPGAs. However, FPGA programming has mainly been reserved for specialists as it is very time consuming. For that reason, the use of FPGAs in areas of scientific HPC is still rare today.
In the HPC environment, there are various programming models for heterogeneous systems offering certain types of accelerators. Common models include OpenCL (http://www.opencl.org), OpenACC (https://www.openacc.org) and OpenMP (https://www.OpenMP.org). These standards, however, are not yet available for the use with FPGAs.

Goals of the ORKA project are:

  1. Development of an OpenMP 4.0 compiler targeting heterogeneous computing platforms with FPGA accelerators in order to simplify the usage of such systems.
  2. Design and implementation of a source-to-source framework transforming C/C++ code with OpenMP 4.0 directives into executable programs utilizing both the host CPU and an FPGA.
  3. Utilization (and improvement) of existing algorithms mapping program code to FPGA hardware.
  4. Development of new (possibly heuristic) methods to optimize programs for inherently parallel architectures.

In 2018, the following important contributions were made:
  • Development of a source-to-source compiler prototype for the rewriting of OpenMP C source code (cf. goal 2).
  • Development of an HLS compiler prototype capable of translating C code into hardware. This prototype later served as starting point for the work towards the goals 3 and 4.
  • Development of several experimental FPGA infrastructures for the execution of accelerator cores (necessary for the goals 1 and 2).
In 2019, the following significant contributions were achieved:
  • Publication of two peer-reviewed papers: "OpenMP on FPGAs - A Survey" and "OpenMP to FPGA Offloading Prototype using OpenCL SDK".
  • Improvement of the source-to-source compiler in order to properly support OpenMP-target-outlining for FPGA targets (incl. smoke tests).
  • Completion of the first working ORKA-HPC prototype supporting a complete OpenMP-to-FPGA flow.
  • Formulation of a genome for the pragma-based genetic optimization of the high-level synthesis step during the ORKA-HPC flow.
  • Extension of the TaPaSCo composer to allow for hardware synchronization primitives inside of TaPaSCo systems.
In 2020, the following significant contributions were achieved:
  • Improvement of the Genetic Optimization.
  • Engineering of a Docker container for reliable reproduction of results.
  • Integration of software components from project partners.
  • Development of a plugin architecture for Low-Level-Platforms.
  • Implementation and integration of two LLP plugin components.
  • Broadening of the accepted subset of OpenMP.
  • Enhancement of the test suite.
In 2021, the following significant contributions were achieved:
  • Enhancement of the benchmark suite.
  • Enhancement of the test suite.
  • Successful project completion with live demo for the project sponsor.
  • Publication of the paper "ORKA-HPC - Practical OpenMP for FPGAs".
  • Release of the source code and the reproduction package.
  • Enhancement of the accepted OpenMP subset with new clauses to control the FPGA related transformations.
  • Improvement of the Genetic Optimization.
  • Comparison of the estimated performance data given by the HLS and the real performance.
  • Synthesis of a linear regression model for performance prediction based on that comparison.
  • Implementation of an infrastructure for the translation of OpenMP reduction clauses.
  • Automated translation of the OpenMP pragma `parallel for` into a parallel FPGA system.
In 2022, the following significant contributions were achieved:
  • Generation and publication of an extensive dataset on HLS area estimates and actual performance.
  • Creation and comparative evaluation of different regression models to predict actual system performance from early (area) estimates.
  • Evaluation of the area estimates generated by the HLS.
  • Publication of the paper “Reducing OpenMP to FPGA Round-trip Times with Predictive Modelling”.
  • Development of a method to detect and remove redundant read operations in FPGA stencil codes based on the polyhedral model.
  • Implementation of the method for ORKA-HPC.
  • Quantitative evaluation of that method to show the strength of the method and to show when to use it.
  • Publication of the paper “Employing Polyhedral Methods to Reduce Data Movement in FPGA Stencil Codes”.