With computing systems becoming ubiquitous, numerous data sets of extremely large size are becoming available for analysis. Often the data collected have complex, graph based structures, which makes them difficult to process with traditional tools. Moreover, the irregularities in the data sets, and in the analysis algorithms, hamper the scaling of performance in large distributed high-performance systems, optimized for locality exploitation and regular data structures. In this paper we present an approach to system design that enable efficient execution of applications with irregular memory patterns on a distributed, many-core architecture, based on off-the-shelf cores. We introduce a set of hardware and software components, which provide a distributed global address space, fine-grained synchronization and latency hiding of remote accesses with multithreading. An FPGA prototype has been implemented to explore the design with a set of typical irregular kernels. We finally present an analytical model that highlights the benefits of the approach and helps identifying the bottlenecks in the prototype. The experimental evaluation on graph based applications demonstrates the scalability of the architecture for different configurations of the whole system.

The recent emergence of large-scale knowledge discovery, data mining and social network analysis, irregular applications have gained renewed interest. Cache-based architectures do not provide optimal performances with such workloads, mainly due to the low spatial and temporal locality of their control and memory access patterns. This paper presents a multi-node, multi-core, multi-threaded shared-memory system architecture designed for the execution of large-scale irregular applications, and built on top of three pillars that support these workloads. First, transparent hardware support for Partitioned Global Address Space (PGAS) provides a large globally-shared address space with no software library overhead. Second, multithreaded multi-core processing nodes achieve the necessary latency tolerance required when accessing physically distributed global memory. Third, hardware support is provided for inter-thread synchronization on the global address space. An analytical performance model that accounts for the main architecture and application characteristics is presented. The hardware design of the proposed custom architectural building blocks is then described. Finally, a multi-board FPGA prototype of the proposed system with typical irregular kernels and benchmarks is presented. The experimental evaluation demonstrates the architecture performance scalability for different configurations of the whole system.

We propose an intermediate approach between full custom hardware systems and full-software tools. Figure 1 shows the overview of the proposed architecture. We start from an off-the-shelf architecture composed of simple, in-order cores and an on-chip interconnection. The onchip interconnection interfaces the processing core with the memory controller for the external memory (DDR3) and the shared I/O peripherals. We add three custom components: the Global Memory Access Scheduler (GMAS), the Global Network Interface (GNI) and the Global SYNChronization module (GSYNC). The GMAS enables support for the scrambled address space. It also implements part of the support latency tolerance, storing remote memory operations, and acts as a scheduler for lightweight software multithreading.

In this paper, we apply multi-objective evolutionary computation to the synthesis of real-time, embedded, heterogeneous, multiprocessor systems (briefly, Multiprocessor Systems-on-Chip or MP-SoCs). Our approach simultaneously explores the architecture, the mapping and the scheduling of the system, by using multi-objective evolution. In particular, we considered three approaches: a multi-objective genetic algorithm, multi-objective Simulated Annealing, and multi-objective Tabu Search. The algorithms search for optimal architectures, in terms of processing elements (processors and hardware accelerators) and communication infrastructure, and for the best mappings and schedules of multi-rate real-time applications given objectives such as: system area, hard and soft dead-lines violations, dimensions of memory buffers. We formalize the problem, describe our flow and compare the three algorithms, dis- cussing which one performs better with respect to different classes of applications.

Multiprocessors on a chip are the reality of these days. Semiconductor industry has recognized this approach as the most efficient in order to exploit chip resources, but the success of this paradigm heavily relies on the efficiency and widespread diffusion of parallel software. Among the many techniques to express the parallelism of applications, this paper focuses on pipelining, a technique well suited to data-intensive multimedia applications. We introduce a prototyping platform (FPGA-based) and a methodology for these applications. Our platform consists of a mix of standard and custom heterogeneous cores. We discuss several case studies, analyzing the interaction of the architecture and applications and we show that multimedia and telecommunication applications with unbalanced pipeline stages can be easily deployed. Our framework eases the development cycle and enables the developers to focus directly on the problems posed by the programming model in the direction of the implementation of a production system.

This paper presents the implementation of a dual-priority scheduling algorithm for real-time embedded systems on a shared memory multiprocessor on FPGA. The dual-priority microkernel is supported by a multiprocessor interrupt controller to trigger periodic and aperiodic thread activation and manage context switching. We show how the dual-priority algorithm performs on a real system prototype compared to the theoretical performance simulations with a typical standard workload of automotive applications, underlining where the differences are.