Search

The goal of modern high performance computing platforms is to combine low power consumption and high throughput. Within the European Processor Initiative (EPI), such an SoC platform to meet the novel exascale requirements is built and investigated. As part of this project, we introduce an embedded Field Programmable Gate Array (eFPGA), adding flexibility to accelerate various workloads. In this article, we show our approach to design the eFPGA tile that supports the EPI SoC. While eFPGAs are inherently reconfigurable, their initial design has to be determined for tape-out. The design space of the eFPGA is explored and evaluated with different configurations of two HPC workloads, covering control and dataflow heavy applications. As a result, we present a well-balanced eFPGA design that can host several use cases and potential future ones by allocating 1% of the total EPI SoC area. Finally, our simulation results of the architectures on the eFPGA show great performance improvements over their software counterparts. European Processor Initiative (EPI) project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 826647, from Spanish Government (PID2019- 107255GB-C21/AEI /10.13039/501100011033), and from Generalitat de Catalunya (contracts 2017-SGR-1414 and 2017-SGR-1328). M. Moreto is partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under Ramon y Cajal fellowship No. RYC-2016-21104.

Nowadays, a new parallel paradigm for energy-efficient heterogeneous hardware infrastructures is required to achieve better performance at a reasonable cost on high-performance computing applications. Under this new paradigm, some application parts are offloaded to specialized accelerators that run faster or are more energy-efficient than CPUs. Field-Programmable Gate Arrays (FPGA) are one of those types of accelerators that are becoming widely available in data centers. This paper proposes OmpSs@cloudFPGA, which includes novel extensions to parallel task-based programming models that enable easy and efficient programming of heterogeneous clusters with FPGAs. The programmer only needs to annotate, with OpenMP-like pragmas, the tasks of the application that should be accelerated in the cluster of FPGAs. Next, the proposed programming model framework automatically extracts parts annotated with High-Level Synthesis (HLS) pragmas and synthesizes them into hardware accelerator cores for FPGAs. Additionally, our extensions include and support two novel features: 1) FPGA-to-FPGA direct communication using a Message Passing Interface (MPI) similar Application Programming Interface (API) with one-to-one and collective communications to alleviate host communication channel bottleneck, and 2) creating and spawning work from inside the FPGAs to their own accelerator cores based on an MPI rank-like identification. These features break the classical host-accelerator model, where the host (typically the CPU) generates all the work and distributes it to each accelerator. We also present an evaluation of OmpSs@cloudFPGA for different parallel strategies of the N-Body application on the IBM cloudFPGA research platform. Results show that for cluster sizes up to 56 FPGAs, the performance scales linearly. To the best of our knowledge, this is the best performance obtained for N-body over FPGA platforms, reaching 344 Gpairs/s with 56 FPGAs. Finally, we compare the performance and power consumption of the proposed approach with the ones obtained by a classical execution on the MareNostrum 4 supercomputer, demonstrating that our FPGA approach reduces power consumption by an order of magnitude. This work has been done in the context of the IBM/BSC Deep Learning Center initiative. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 754337 (EuroEXA), from Spanish Government (PID2019-107255GBC21/AEI/10.13039/501100011033), and from Generalitat de Catalunya (2017-SGR-1414 and 2017-SGR-1328).

This paper presents the new features of the OmpSs@FPGA framework. OmpSs is a data-flow programming model that supports task nesting and dependencies to target asynchronous parallelism and heterogeneity. OmpSs@FPGA is the extension of the programming model addressed specifically to FPGAs. OmpSs environment is built on top of Mercurium source to source compiler and Nanos++ runtime system. To address FPGA specifics Mercurium compiler implements several FPGA related features as local variable caching, wide memory accesses or accelerator replication. In addition, part of the Nanos++ runtime has been ported to hardware. Driven by the compiler this new hardware runtime adds new features to FPGA codes, such as task creation and dependence management, providing both performance increases and ease of programming. To demonstrate these new capabilities, different high performance benchmarks have been evaluated over different FPGA platforms using the OmpSs programming model. The results demonstrate that programs that use the OmpSs programming model achieve very competitive performance with low to moderate porting effort compared to other FPGA implementations. This work has received funding from EuroEXA project (European Union’s Horizon 2020 Research and Innovation Programme, under grant agreement No 754337), from Spanish Government (projects PID2019-107255GB and SEV-2015- 0493, grant BES-2016-078046), and Generalitat de Catalunya (contracts 2017-SGR-1414 and 2017-SGR-1328).