An Inter-Processor Communication (IPC) Data Sharing Architecture in Heterogeneous MPSoC for OFDMA

Multiprocessor system-on-chip (MPSoC) promises better data management for parallel processing than conventional SoC. This feature is very suitable for wireless communication systems. Better data processing management can reduce resource utilization and can potentially reduce power consumption as well. Hence, this research aimed to minimize the orthogonal frequency-division multiple access (OFDMA) processing hardware by proposing a new data sharing architecture on a heterogeneous MPSoC platform that incorporates inter-processor communication (IPC), multi-processor, multi-bus, multi-frequency and parallel processing design of the medium access controller (MAC) layer. This MPSoC was designed based on a RISC processor with an AMBA multi-bus system. To achieve high throughput, the proposed MPSoC runs at two different frequencies, 40 MHz and 80 MHz. The proposed system was implemented and verified using FPGA. The verification results showed that the proposed system can work in real-time with a maximum throughput of 11 MBps using a 40 MHz system clock. The proposed MPSoC is a promising solution to perform OFDMA processing on 4G and 5G technologies

Self-adaptivity of applications on network on chip multiprocessors: the case of fault-tolerant Kahn process networks

Technology scaling accompanied with higher operating frequencies and the ability to integrate more functionality in the same chip has been the driving force behind delivering higher performance computing systems at lower costs. Embedded computing systems, which have been riding the same wave of success, have evolved into complex architectures encompassing a high number of cores interconnected by an on-chip network (usually identified as Multiprocessor System-on-Chip). However these trends are hindered by issues that arise as technology scaling continues towards deep submicron scales. Firstly, growing complexity of these systems and the variability introduced by process technologies make it ever harder to perform a thorough optimization of the system at design time. Secondly, designers are faced with a reliability wall that emerges as age-related degradation reduces the lifetime of transistors, and as the probability of defects escaping post-manufacturing testing is increased. In this thesis, we take on these challenges within the context of streaming applications running in network-on-chip based parallel (not necessarily homogeneous) systems-on-chip that adopt the no-remote memory access model. In particular, this thesis tackles two main problems: (1) fault-aware online task remapping, (2) application-level self-adaptation for quality management. For the former, by viewing fault tolerance as a self-adaptation aspect, we adopt a cross-layer approach that aims at graceful performance degradation by addressing permanent faults in processing elements mostly at system-level, in particular by exploiting redundancy available in multi-core platforms. We propose an optimal solution based on an integer linear programming formulation (suitable for design time adoption) as well as heuristic-based solutions to be used at run-time. We assess the impact of our approach on the lifetime reliability. We propose two recovery schemes based on a checkpoint-and-rollback and a rollforward technique. For the latter, we propose two variants of a monitor-controller- adapter loop that adapts application-level parameters to meet performance goals. We demonstrate not only that fault tolerance and self-adaptivity can be achieved in embedded platforms, but also that it can be done without incurring large overheads. In addressing these problems, we present techniques which have been realized (depending on their characteristics) in the form of a design tool, a run-time library or a hardware core to be added to the basic architecture

System-Level Design and Virtual Prototyping of a Telecommunication Application on a NUMA Platform

International audienceThe use of model-driven approaches for embedded system design has become a common practice. Among these model-driven approaches, only a few of them include the generation of a full-system simulation comprising operating system, code generation for tasks and hardware simulation models. Even less common is the extension to massively parallel, NoC based designs, such as required for high performance streaming applications where dozens of tasks are replicated onto identical general purpose processor cores of a Multi-processor System-on-chip (MP-SoC). We present the extension of a system-level tool to handle clustered Network-on-Chip (NoC) with virtual prototyping platforms. On the one hand, the automatic generation of the virtual prototype becomes more complex as topcell, address mapping and linker script have to be adapted. On the other hand, the exploration of the design space is particularly important for this class of applications, as performance may strongly be impacted by Non Uniform Memory Access (NUMA)

Integrated support for Adaptivity and Fault-tolerance in MPSoCs

The technology improvement and the adoption of more and more complex applications in consumer electronics are forcing a rapid increase in the complexity of multiprocessor systems on chip (MPSoCs). Following this trend, MPSoCs are becoming increasingly dynamic and adaptive, for several reasons. One of these is that applications are getting intrinsically dynamic. Another reason is that the workload on emerging MPSoCs cannot be predicted because modern systems are open to new incoming applications at run-time. A third reason which calls for adaptivity is the decreasing component reliability associated with technology scaling. Components below the 32-nm node are more inclined to temporal or even permanent faults. In case of a malfunctioning system component, the rest of the system is supposed to take over its tasks. Thus, the system adaptivity goal shall influence several de- sign decisions, that have been listed below: 1) The applications should be specified such that system adaptivity can be easily supported. To this end, we consider Polyhedral Process Networks (PPNs) as model of computation to specify applications. PPNs are composed by concurrent and autonomous processes that communicate between each other using bounded FIFO channels. Moreover, in PPNs the control is completely distributed, as well as the memories. This represents a good match with the emerging MPSoC architectures, in which processing elements and memories are usually distributed. Most importantly, the simple operational semantics of PPNs allows for an easy adoption of system adaptivity mechanisms. 2) The hardware platform should guarantee the flexibility that adaptivity mechanisms require. Networks-on-Chip (NoCs) are emerging communication infrastructures for MPSoCs that, among many other advantages, allow for system adaptivity. This is because NoCs are generic, since the same platformcan be used to run different applications, or to run the same application with different mapping of processes. However, there is a mismatch between the generic structure of the NoCs and the semantics of the PPN model. Therefore, in this thesis we investigate and propose several communication approaches to overcome this mismatch. 3) The system must be able to change the process mapping at run-time, using process migration. To this end, a process migration mechanism has been proposed and evaluated. This mechanism takes into account specific requirements of the embedded domain such as predictability and efficiency. To face the problem of graceful degradation of the system, we enriched the MADNESS NoC platform by adding fault tolerance support at both software and hardware level. The proposed process migration mechanism can be exploited to cope with permanent faults by migrating the processes running on the faulty processing element. A fast heuristic is used to determine the new mapping of the processes to tiles. The experimental results prove that the overhead in terms of execution time, due to the execution time of the remapping heuristic, together with the actual process migration, is almost negligible compared to the execution time of the whole application. This means that the proposed approach allows the system to change its performance metrics and to react to faults without a substantial impact on the user experience

Integrated support for Adaptivity and Fault-tolerance in MPSoCs

The technology improvement and the adoption of more and more complex applications in consumer electronics are forcing a rapid increase in the complexity of multiprocessor systems on chip (MPSoCs). Following this trend, MPSoCs are becoming increasingly dynamic and adaptive, for several reasons. One of these is that applications are getting intrinsically dynamic. Another reason is that the workload on emerging MPSoCs cannot be predicted because modern systems are open to new incoming applications at run-time. A third reason which calls for adaptivity is the decreasing component reliability associated with technology scaling. Components below the 32-nm node are more inclined to temporal or even permanent faults. In case of a malfunctioning system component, the rest of the system is supposed to take over its tasks. Thus, the system adaptivity goal shall influence several de- sign decisions, that have been listed below: 1) The applications should be specified such that system adaptivity can be easily supported. To this end, we consider Polyhedral Process Networks (PPNs) as model of computation to specify applications. PPNs are composed by concurrent and autonomous processes that communicate between each other using bounded FIFO channels. Moreover, in PPNs the control is completely distributed, as well as the memories. This represents a good match with the emerging MPSoC architectures, in which processing elements and memories are usually distributed. Most importantly, the simple operational semantics of PPNs allows for an easy adoption of system adaptivity mechanisms. 2) The hardware platform should guarantee the flexibility that adaptivity mechanisms require. Networks-on-Chip (NoCs) are emerging communication infrastructures for MPSoCs that, among many other advantages, allow for system adaptivity. This is because NoCs are generic, since the same platformcan be used to run different applications, or to run the same application with different mapping of processes. However, there is a mismatch between the generic structure of the NoCs and the semantics of the PPN model. Therefore, in this thesis we investigate and propose several communication approaches to overcome this mismatch. 3) The system must be able to change the process mapping at run-time, using process migration. To this end, a process migration mechanism has been proposed and evaluated. This mechanism takes into account specific requirements of the embedded domain such as predictability and efficiency. To face the problem of graceful degradation of the system, we enriched the MADNESS NoC platform by adding fault tolerance support at both software and hardware level. The proposed process migration mechanism can be exploited to cope with permanent faults by migrating the processes running on the faulty processing element. A fast heuristic is used to determine the new mapping of the processes to tiles. The experimental results prove that the overhead in terms of execution time, due to the execution time of the remapping heuristic, together with the actual process migration, is almost negligible compared to the execution time of the whole application. This means that the proposed approach allows the system to change its performance metrics and to react to faults without a substantial impact on the user experience

Adaptivity Support for MPSoCs Based on Process Migration in Polyhedral Process Networks

987209Computer System

Mixing multi-core CPUs and GPUs for scientific simulation software

Recent technological and economic developments have led to widespread availability of multi-core CPUs and specialist accelerator processors such as graphical processing units (GPUs). The accelerated computational performance possible from these devices can be very high for some applications paradigms. Software languages and systems such as NVIDIA's CUDA and Khronos consortium's open compute language (OpenCL) support a number of individual parallel application programming paradigms. To scale up the performance of some complex systems simulations, a hybrid of multi-core CPUs for coarse-grained parallelism and very many core GPUs for data parallelism is necessary. We describe our use of hybrid applica- tions using threading approaches and multi-core CPUs to control independent GPU devices. We present speed-up data and discuss multi-threading software issues for the applications level programmer and o er some suggested areas for language development and integration between coarse-grained and ne-grained multi-thread systems. We discuss results from three common simulation algorithmic areas including: partial di erential equations; graph cluster metric calculations and random number generation. We report on programming experiences and selected performance for these algorithms on: single and multiple GPUs; multi-core CPUs; a CellBE; and using OpenCL. We discuss programmer usability issues and the outlook and trends in multi-core programming for scienti c applications developers