In order to reach exascale performance, current HPC systems need to be improved. Simple hardware scaling is not a feasible solution due to the increasing utility costs and power consumption limitations. Apart from improvements in implementation technology, what is needed is to refine the HPC application development flow as well as the system architecture of future HPC systems. ECOSCALE tackles these challenges by proposing a scalable programming environment and architecture, aiming to substantially reduce energy consumption as well as data traffic and latency. ECOSCALE introduces a novel heterogeneous energy-efficient hierarchical architecture, as well as a hybrid many-core+OpenCL programming environment and runtime system. The ECOSCALE approach is hierarchical and is expected to scale well by partitioning the physical system into multiple independent Workers (i.e. compute nodes). Workers are interconnected in a tree-like fashion and define a contiguous global address space that can be viewed either as a set of partitions in a Partitioned Global Address Space (PGAS), or as a set of nodes hierarchically interconnected via an MPI protocol. To further increase energy efficiency, as well as to provide resilience, the Workers employ reconfigurable accelerators mapped into the virtual address space utilizing a dual stage System Memory Management Unit with coherent memory access. The architecture supports shared partitioned reconfigurable resources accessed by any Worker in a PGAS partition, as well as automated hardware synthesis of these resources from an OpenCL-based programming model.

Efficient prototyping of a large complex system can be significantly facilitated by the use of a flexible and versatile physical platform where both new hardware and software components can readily be implemented and tightly integrated in a timely manner. Towards this end, we have developed the 120 130 mm QFDB board and associated firmware, including the system software environment. We developed a large system based on this advanced dense and modular building block. The QFDB features 4 interconnected Xilinx Zynq Ultrascale+ devices, each one consisting of an ARM-based subsystem tightly coupled with reconfigurable logic. Each Zynq Ultrascale+ is connected to 16 GB of DDR4 memory. In addition, one Zynq provides storage through an M.2 Solid State Disk (SSD). In this paper, we present the design and the implementation of this board, as well as the software environment for board operation. Moreover, we describe a 10 Gb Ethernet communication infrastructure for interconnecting multiple boards together. Finally, we highlight the impact of this board on a number of ongoing research activities that leverage the QFDB versatility, both as a largescale prototyping system for HPC solutions, and as a host for the development of FPGA integration techniques.

Todaypsilas verification challenges require high-performance simulation solutions, such as hardware simulation accelerators and emulators, that have been in use in hardware and electronic system design centers for approximately the last decade. In particular, in order to accelerate functional simulation, hardware emulation is used so as to offload calculation-intensive tasks from the software simulator. However, the communication overhead between the software simulator and the hardware emulator is becoming a new critical bottleneck. In our work we introduce a novel way of repartitioning the simulation between software and hardware in order to minimize this communication bottleneck. Using the techniques described in this paper we are able to offload a big part of the work that is traditionally done by the software simulator, onto the hardware emulator. Our experiments, using real-world designs, demonstrate that the proposed method reduces significantly the communication overhead and outperforms the conventional hardware emulation systems by a factor of more than 7. Finally, we provide a way of observing and modifying the internal state of the hardware emulator while the test is running.

Using FPGAs as hardware accelerators that communicate with a central CPU is becoming a common practice in the embedded design world but there is no standard methodology and toolset to facilitate this path yet. On the other hand, languages such as CUDA and OpenCL provide standard development environments for Graphical Processing Unit (GPU) programming. FASTCUDA is a platform that provides the necessary software toolset, hardware architecture, and design methodology to efficiently adapt the CUDA approach into a new FPGA design flow. With FASTCUDA, the CUDA kernels of a CUDA-based application are partitioned into two groups with minimal user intervention: those that are compiled and executed in parallel software, and those that are synthesized and implemented in hardware. A modern low power FPGA can provide the processing power (via numerous embedded micro-CPUs) and the logic capacity for both the software and hardware implementations of the CUDA kernels. This paper describes the system requirements and the architectural decisions behind the FASTCUDA approach.