Runtime Scheduling, Allocation, and Execution of Real-Time Hardware Tasks onto Xilinx FPGAs Subject to Fault Occurrence

This paper describes a novel way to exploit the computation capabilities delivered by modern Field-Programmable Gate Arrays (FPGAs), not only towards a higher performance, but also towards an improved reliability. Computation-specific pieces of circuitry are dynamically scheduled and allocated to different resources on the chip based on a set of novel algorithms which are described in detail in this article. These algorithms consider most of the technological constraints existing in modern partially reconfigurable FPGAs as well as spontaneously occurring faults and emerging permanent damage in the silicon substrate of the chip. In addition, the algorithms target other important aspects such as communications and synchronization among the different computations that are carried out, either concurrently or at different times. The effectiveness of the proposed algorithms is tested by means of a wide range of synthetic simulations, and, notably, a proof-of-concept implementation of them using real FPGA hardware is outlined

Assessing Defragmentation Strategies for FPGAs

Fragmentation on dynamically reconfigurable FPGAs is currently a major obstacle to the efficient management of its logic space. When resource allocation decisions have to be made at run-time a relocation of currently running functions may be necessary to release enough contiguous resources to implement incoming functions.Relocation should take into account any specifics of functions functionality and also those of the FPGAs architecture as to not affect systems performance. A simple and fast method to assess performance degradation of a function during relocation is proposed in this paper. This method is based on previous function labelling and on the concept of proximity vectors

Design and resource management of reconfigurable multiprocessors for data-parallel applications

FPGA (Field-Programmable Gate Array)-based custom reconfigurable computing machines have established themselves as low-cost and low-risk alternatives to ASIC (Application-Specific Integrated Circuit) implementations and general-purpose microprocessors in accelerating a wide range of computation-intensive applications. Most often they are Application Specific Programmable Circuiits (ASPCs), which are developer programmable instead of user programmable. The major disadvantages of ASPCs are minimal programmability, and significant time and energy overheads caused by required hardware reconfiguration when the problem size outnumbers the available reconfigurable resources; these problems are expected to become more serious with increases in the FPGA chip size. On the other hand, dominant high-performance computing systems, such as PC clusters and SMPs (Symmetric Multiprocessors), suffer from high communication latencies and/or scalability problems. This research introduces low-cost, user-programmable and reconfigurable MultiProcessor-on-a-Programmable-Chip (MPoPC) systems for high-performance, low-cost computing. It also proposes a relevant resource management framework that deals with performance, power consumption and energy issues. These semi-customized systems reduce significantly runtime device reconfiguration by employing userprogrammable processing elements that are reusable for different tasks in large, complex applications. For the sake of illustration, two different types of MPoPCs with hardware FPUs (floating-point units) are designed and implemented for credible performance evaluation and modeling: the coarse-grain MIMD (Multiple-Instruction, Multiple-Data) CG-MPoPC machine based on a processor IP (Intellectual Property) core and the mixed-mode (MIMD, SIMD or M-SIMD) variant-grain HERA (HEterogeneous Reconfigurable Architecture) machine. In addition to alleviating the above difficulties, MPoPCs can offer several performance and energy advantages to our data-parallel applications when compared to ASPCs; they are simpler and more scalable, and have less verification time and cost. Various common computation-intensive benchmark algorithms, such as matrix-matrix multiplication (MMM) and LU factorization, are studied and their parallel solutions are shown for the two MPoPCs. The performance is evaluated with large sparse real-world matrices primarily from power engineering. We expect even further performance gains on MPoPCs in the near future by employing ever improving FPGAs. The innovative nature of this work has the potential to guide research in this arising field of high-performance, low-cost reconfigurable computing. The largest advantage of reconfigurable logic lies in its large degree of hardware customization and reconfiguration which allows reusing the resources to match the computation and communication needs of applications. Therefore, a major effort in the presented design methodology for mixed-mode MPoPCs, like HERA, is devoted to effective resource management. A two-phase approach is applied. A mixed-mode weighted Task Flow Graph (w-TFG) is first constructed for any given application, where tasks are classified according to their most appropriate computing mode (e.g., SIMD or MIMD). At compile time, an architecture is customized and synthesized for the TFG using an Integer Linear Programming (ILP) formulation and a parameterized hardware component library. Various run-time scheduling schemes with different performanceenergy objectives are proposed. A system-level energy model for HERA, which is based on low-level implementation data and run-time statistics, is proposed to guide performance-energy trade-off decisions. A parallel power flow analysis technique based on Newton\u27s method is proposed and employed to verify the methodology

Efficient runtime placement management for high performance and reliability in COTS FPGAs

Designing high-performance, fault-tolerant multisensory electronic systems for hostile environments such as nuclear plants and outer space within the constraints of cost, power and flexibility is challenging. Issues such as ionizing radiation, extreme temperature and ageing can lead to faults in the electronics of these systems. In addition, the remote nature of these environments demands a level of flexibility and autonomy in their operations. The standard practice of using specially hardened electronic devices for such systems is not only very expensive but also has limited flexibility. This thesis proposes novel techniques that promote the use of Commercial Off-The- Shelf (COTS) reconfigurable devices to meet the challenges of high-performance systems for hostile environments. Reconfigurable hardware such as Field Programmable Gate Arrays (FPGA) have a unique combination of flexibility and high performance. The flexibility offered through features such as dynamic partial reconfiguration (DPR) can be harnessed not only to achieve cost-effective designs as a smaller area can be used to execute multiple tasks, but also to improve the reliability of a system as a circuit on one portion of the device can be physically relocated to another portion in the case of fault occurrence. However, to harness these potentials for high performance and reliability in a cost-effective manner, novel runtime management tools are required. Most runtime support tools for reconfigurable devices are based on ideal models which do not adequately consider the limitations of realistic FPGAs, in particular modern FPGAs which are increasingly heterogeneous. Specifically, these tools lack efficient mechanisms for ensuring a high utilization of FPGA resources, including the FPGA area and the configuration port and clocking resources, in a reliable manner. To ensure high utilization of reconfigurable device area, placement management is a key aspect of these tools. This thesis presents novel techniques for the management of hardware task placement on COTS reconfigurable devices for high performance and reliability. To this end, it addresses design-time issues that affect efficient hardware task placement, with a focus on reliability. It also presents techniques to maximize the utilization of the FPGA area in runtime, including techniques to minimize fragmentation. Fragmentation leads to the creation of unusable areas due to dynamic placement of tasks and the heterogeneity of the resources on the chip. Moreover, this thesis also presents an efficient task reuse mechanism to improve the availability of the internal configuration infrastructure of the FPGA for critical responsibilities like error mitigation. The task reuse scheme, unlike previous approaches, also improves the utilization of the chip area by offering defragmentation. Task relocation, which involves changing the physical location of circuits is a technique for error mitigation and high performance. Hence, this thesis also provides a functionality-based relocation mechanism for improving the number of locations to which tasks can be relocated on heterogeneous FPGAs. As tasks are relocated, clock networks need to be routed to them. As such, a reliability-aware technique of clock network routing to tasks after placement is also proposed. Finally, this thesis offers a prototype implementation and characterization of a placement management system (PMS) which is an integration of the aforementioned techniques. The performance of most of the proposed techniques are tested using data processing tasks of a NASA JPL spectrometer application. The results show that the proposed techniques have potentials to improve the reliability and performance of applications in hostile environment compared to state-of-the-art techniques. The task optimization technique presented leads to better capacity to circumvent permanent faults on COTS FPGAs compared to state-of-the-art approaches (48.6% more errors were circumvented for the JPL spectrometer application). The proposed task reuse scheme leads to approximately 29% saving in the amount of configuration time. This frees up the internal configuration interface for more error mitigation operations. In addition, the proposed PMS has a worst-case latency of less than 50% of that of state-of- the-art runtime placement systems, while maintaining the same level of placement quality and resource overhead

Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

Towards the development of a reliable reconfigurable real-time operating system on FPGAs

In the last two decades, Field Programmable Gate Arrays (FPGAs) have been rapidly developed from simple “glue-logic” to a powerful platform capable of implementing a System on Chip (SoC). Modern FPGAs achieve not only the high performance compared with General Purpose Processors (GPPs), thanks to hardware parallelism and dedication, but also better programming flexibility, in comparison to Application Specific Integrated Circuits (ASICs). Moreover, the hardware programming flexibility of FPGAs is further harnessed for both performance and manipulability, which makes Dynamic Partial Reconfiguration (DPR) possible. DPR allows a part or parts of a circuit to be reconfigured at run-time, without interrupting the rest of the chip’s operation. As a result, hardware resources can be more efficiently exploited since the chip resources can be reused by swapping in or out hardware tasks to or from the chip in a time-multiplexed fashion. In addition, DPR improves fault tolerance against transient errors and permanent damage, such as Single Event Upsets (SEUs) can be mitigated by reconfiguring the FPGA to avoid error accumulation. Furthermore, power and heat can be reduced by removing finished or idle tasks from the chip. For all these reasons above, DPR has significantly promoted Reconfigurable Computing (RC) and has become a very hot topic. However, since hardware integration is increasing at an exponential rate, and applications are becoming more complex with the growth of user demands, highlevel application design and low-level hardware implementation are increasingly separated and layered. As a consequence, users can obtain little advantage from DPR without the support of system-level middleware. To bridge the gap between the high-level application and the low-level hardware implementation, this thesis presents the important contributions towards a Reliable, Reconfigurable and Real-Time Operating System (R3TOS), which facilitates the user exploitation of DPR from the application level, by managing the complex hardware in the background. In R3TOS, hardware tasks behave just like software tasks, which can be created, scheduled, and mapped to different computing resources on the fly. The novel contributions of this work are: 1) a novel implementation of an efficient task scheduler and allocator; 2) implementation of a novel real-time scheduling algorithm (FAEDF) and two efficacious allocating algorithms (EAC and EVC), which schedule tasks in real-time and circumvent emerging faults while maintaining more compact empty areas. 3) Design and implementation of a faulttolerant microprocessor by harnessing the existing FPGA resources, such as Error Correction Code (ECC) and configuration primitives. 4) A novel symmetric multiprocessing (SMP)-based architectures that supports shared memory programing interface. 5) Two demonstrations of the integrated system, including a) the K-Nearest Neighbour classifier, which is a non-parametric classification algorithm widely used in various fields of data mining; and b) pairwise sequence alignment, namely the Smith Waterman algorithm, used for identifying similarities between two biological sequences. R3TOS gives considerably higher flexibility to support scalable multi-user, multitasking applications, whereby resources can be dynamically managed in respect of user requirements and hardware availability. Benefiting from this, not only the hardware resources can be more efficiently used, but also the system performance can be significantly increased. Results show that the scheduling and allocating efficiencies have been improved up to 2x, and the overall system performance is further improved by ~2.5x. Future work includes the development of Network on Chip (NoC), which is expected to further increase the communication throughput; as well as the standardization and automation of our system design, which will be carried out in line with the enablement of other high-level synthesis tools, to allow application developers to benefit from the system in a more efficient manner

Letter from the Special Issue Editor

Editorial work for DEBULL on a special issue on data management on Storage Class Memory (SCM) technologies

Power management techniques for conserving energy in multiple system components

Energy consumption is a limiting constraint for both embedded and high performance systems. CPU-core, caches and memory contribute a large fraction of energy consumption in most computing systems. As a result, reducing the energy consumption in these components can significantly reduce the system's overall energy consumption. However, applying multiple independent power management policies in the system (one for each component) may interfere with each other and in some occasions increase the combined energy consumption.In this dissertation, I present three power management techniques that target more than a single component in a system. The focus is on reducing the total energy consumption in processors, caches and memory combinations. First, I present a memory-aware processor power management using collaboration between the OS and the compiler. The technique objectives are: (1) finish the application execution before its deadline and (2) minimize the combined energy consumption in processor and memory. Second, I present an Integrated Dynamic Voltage Scaling (IDVS) techniques for processor and on-chip cache power management in multi-voltage domains systems. IDVS co-ordinates power management decisions across voltage domains rather that being applied in isolation in each domain. Third, I present a Power-Aware Cached DRAM (PA-CDRAM) memory organization for reducing the energy consumption in DRAM memory and off-chip caches. PA-CDRAM exploits the high internal memory bandwidth by bringing the off-chip caches "closer" to the memory, which also improves the overall performance.The techniques in my thesis highlight the importance of designing power management schemes that consider multiple components and their interactions (in terms of power and performance) in the system rather than applying multiple isolated power management polices. This study should lay the foundation for further research in the domain of integrated power management, where a single power manager controls many system components

Worst-Case Execution Time Guarantees for Runtime-Reconfigurable Architectures

Real-time systems are ubiquitous in our everyday life, e.g., in safety-critical domains such as automotive, avionics or robotics. The correctness of a real-time system does not only depend on the correctness of its calculations, but also on the non-functional requirement of adhering to deadlines. Failing to meet a deadline may lead to severe malfunctions, therefore worst-case execution times (WCET) need to be guaranteed. Despite significant scientific advances, however, timing analysis of WCET guarantees lags years behind current high-performance microarchitectures with out-of-order scheduling pipelines, several hardware threads and multiple (shared) cache layers. To satisfy the increasing performance demands of real-time systems, analyzable performance features are required. In order to escape the scarcity of timing-analyzable performance features, the main contribution of this thesis is the introduction of runtime reconfiguration of hardware accelerators onto a field-programmable gate array (FPGA) as a novel means to achieve performance that is amenable to WCET guarantees. Instead of designing an architecture for a specific application domain, this approach preserves the flexibility of the system. First, this thesis contributes novel co-scheduling approaches to distribute work among CPU and GPU in an extensive analysis of how (average-case) performance is achieved on fused CPU-GPU architectures, a main trend in current high-performance microarchitectures that combines a CPU and a GPU on a single chip. Being able to employ such architectures in real-time systems would be highly desirable, because they provide high performance within a limited area and power budget. As a result of this analysis, however, a cache coherency bottleneck is uncovered in recent fused CPU-GPU architectures that share the last level cache between CPU and GPU. This insight (i) complicates performance predictions and (ii) adds a shared last level cache between CPU and GPU to the growing list of microarchitectural features that benefit average-case performance, but render the analysis of WCET guarantees on high-performance architectures virtually infeasible. Thus, further motivating the need for novel microarchitectural features that provide predictable performance and are amenable to timing analysis. Towards this end, a runtime reconfiguration controller called ``Command-based Reconfiguration Queue\u27\u27 (CoRQ) is presented that provides guaranteed latencies for its operations, especially for the reconfiguration delay, i.e., the time it takes to reconfigure a hardware accelerator onto a reconfigurable fabric (e.g., FPGA). CoRQ enables the design of timing-analyzable runtime-reconfigurable architectures that support WCET guarantees. Based on the --now feasible-- guaranteed reconfiguration delay of accelerators, a WCET analysis is introduced that enables tasks to reconfigure application-specific custom instructions (CIs) at runtime. CIs are executed by a processor pipeline and invoke execution of one or more accelerators. Different measures to deal with reconfiguration delays are compared for their impact on accelerated WCET guarantees and overestimation. The timing anomaly of runtime reconfiguration is identified and safely bounded: a case where executing iterations of a computational kernel faster than in WCET during reconfiguration of CIs can prolong the total execution time of a task. Once tasks that perform runtime reconfiguration of CIs can be analyzed for WCET guarantees, the question of which CIs to configure on a constrained reconfigurable area to optimize the WCET is raised. The question is addressed for systems where multiple CIs with different implementations each (allowing to trade-off latency and area requirements) can be selected. This is generally the case, e.g., when employing high-level synthesis. This so-called WCET-optimizing instruction set selection problem is modeled based on the Implicit Path Enumeration Technique (IPET), which is the path analysis technique state-of-the-art timing analyzers rely on. To our knowledge, this is the first approach that enables WCET optimization with support for making use of global program flow information (and information about reconfiguration delay). An optimal algorithm (similar to Branch and Bound) and a fast greedy heuristic algorithm (that achieves the optimal solution in most cases) are presented. Finally, an approach is presented that, for the first time, combines optimized static WCET guarantees and runtime optimization of the average-case execution (maintaining WCET guarantees) using runtime reconfiguration of hardware accelerators by leveraging runtime slack (the amount of time that program parts are executed faster than in WCET). It comprises an analysis of runtime slack bounds that enable safe reconfiguration for average-case performance under WCET guarantees and presents a mechanism to monitor runtime slack using a simple performance counter that is commonly available in many microprocessors. Ultimately, this thesis shows that runtime reconfiguration of accelerators is a key feature to achieve predictable performance