The Mont-Blanc 2020 (MB2020) project has triggered the development of the next generation industrial processor for Big Data and High Performance Computing (HPC). MB2020 is paving the way to the future low-power European processor for exascale, defining the System-on-Chip (SoC) architecture and implementing new critical building blocks to be integrated in such an SoC. In this paper, we first present an overview of the MB2020 project, then we describe our experimental infrastructure, the requirements of relevant applications, and the IP blocks developed in the project. Finally, we present our emulation-based final demonstrator and explain how it integrates within our first generation of HPC processors.

Task-based programming models such as OpenMP 5.0 and OmpSs are simple to use and powerful enough to exploit task parallelism of applications over multicore, manycore and heterogeneous systems. However, their software-only runtimes introduce relevant overhead when targeting fine-grained tasks, resulting in performance losses. To overcome this drawback, we present a hardware runtime Picos++ that accelerates critical runtime functions such as task dependence analysis, nested task support, and heterogeneous task scheduling. As a proof-of-concept, the Picos++ hardware runtime has been integrated with a compiler infrastructure that supports parallel task-based programming models. A FPGA SoC running Linux OS has been used to implement the hardware accelerated part of Picos++, integrated with a heterogeneous system composed of 4 symmetric multiprocessor (SMP) cores and several hardware functional accelerators (HwAccs) for task execution. Results show significant improvements on energy and performance compared to state-of-the-art parallel software-only runtimes. With Picos++, applications can achieve up to 7.6x speedup and save up to 90 percent of energy, when using 4 threads and up to 4 HwAccs, and even reach a speedup of 16x over the software alternative when using 12 HwAccs and small tasks.

OmpSs@FPGA is the flavor of OmpSs that allows offloading application functionality to FPGAs. Similarly to OpenMP, it is based on compiler directives. While the OpenMP specification also includes support for heterogeneous execution, we use OmpSs and OmpSs@FPGA as prototype implementation to develop new ideas for OpenMP. OmpSs@FPGA implements the tasking model with runtime support to automatically exploit all SMP and FPGA resources available in the execution platform. In this paper, we present the OmpSs@FPGA ecosystem, based on the Mercurium compiler and the Nanos++ runtime system. We show how the applications are transformed to run on the SMP cores and the FPGA. The application kernels defined as tasks to be accelerated, using the OmpSs directives are: 1) transformed by the compiler into kernels connected with the proper synchronization and communication ports, 2) extracted to intermediate files, 3) compiled through the FPGA vendor HLS tool, and 4) used to configure the FPGA. Our Nanos++ runtime system schedules the application tasks on the platform, being able to use the SMP cores and the FPGA accelerators at the same time. We present the evaluation of the OmpSs@FPGA environment with the Matrix Multiplication, Cholesky and N-Body benchmarks, showing the internal details of the execution, and the performance obtained on a Zynq Ultrascale+ MPSoC (up to 128x). The source code uses OmpSs@FPGA annotations and different Vivado HLS optimization directives are applied for acceleration.

Task-based programming models such as OpenMP, IntelTBB and OmpSs offer the possibility of expressing dependences among tasks to drive their execution at runtime. Managing these dependences introduces noticeable overheads when targeting fine-grained tasks, diminishing the potential speedups or even introducing performance losses. To overcome this drawback, we present a general purpose hardware accelerator, Picos++, to manage the inter-task dependences efficiently in both time and energy. Our design also includes a novel nested task support. To this end, a new hardware/software co-design is presented to overcome the fact that nested tasks with dependences could result in system deadlocks due to the limited amount of resources in hardware task dependence managers. In this paper we describe a detailed implementation of this design and evaluate a parallel task-based programming model using Picos++ in a Linux embedded system with two ARM Cortex-A9 and a FPGA. The scalability and energy consumption of the real system implemented have been studied and compared against a software runtime. Even in a system limited to 2 threads, using Picos++ results in more than 1.8x speedup and 40% of energy savings in the most demanding parallelizations of real benchmarks. As a matter of fact, a hardware task dependence manager should be able to achieve much higher speedup and provide more energy savings with more threads.

Along with the popularity of multicore and manycore, task-based dataflow programming models obtain great attention for being able to extract high parallelism from applications without exposing the complexity to programmers. One of these pioneers is the OpenMP Superscalar (OmpSs). By implementing dynamic task dependence analysis, dataflow scheduling and out-of-order execution in runtime, OmpSs achieves high performance using coarse and medium granularity tasks. In theory, for the same application, the more parallel tasks can be exposed, the higher possible speedup can be achieved. Yet this factor is limited by task granularity, up to a point where the runtime overhead outweighs the performance increase and slows down the application. To overcome this handicap, Picos was proposed to support task-based dataflow programming models like OmpSs as a fast hardware accelerator for fine-grained task and dependence management, and a simulator was developed to perform design space exploration. This paper presents the very first functional hardware prototype inspired by Picos. An embedded system based on a Zynq 7000 All-Programmable SoC is developed to study its capabilities and possible bottlenecks. Initial scalability and hardware consumption studies of different Picos designs are performed to find the one with the highest performance and lowest hardware cost. A further thorough performance study is employed on both the prototype with the most balanced configuration and the OmpSs software-only alternative. Results show that our OmpSs runtime hardware support significantly outperforms the software-only implementation currently available in the runtime system for fine-grained tasks.