HARDWARE DESIGN OF MESSAGE PASSING ARCHITECTURE ON HETEROGENEOUS SYSTEM

Heterogeneous multi/many-core chips are commonly used in today’s top tier supercomputers. Similar heterogeneous processing elements — or, computation ac- celerators — are commonly found in FPGA systems. Within both multi/many-core chips and FPGA systems, the on-chip network plays a critical role by connecting these processing elements together. However, The common use of the on-chip network is for point-to-point communication between on-chip components and the memory in- terface. As the system scales up with more nodes, traditional programming methods, such as MPI, cannot effectively use the on-chip network and the off-chip network, therefore could make communication the performance bottleneck. This research proposes a MPI-like Message Passing Engine (MPE) as part of the on-chip network, providing point-to-point and collective communication primitives in hardware. On one hand, the MPE improves the communication performance by offloading the communication workload from the general processing elements. On the other hand, the MPE provides direct interface to the heterogeneous processing ele- ments which can eliminate the data path going around the OS and libraries. Detailed experimental results have shown that the MPE can significantly reduce the com- munication time and improve the overall performance, especially for heterogeneous computing systems because of the tight coupling with the network. Additionally, a hybrid “MPI+X” computing system is tested and it shows MPE can effectively of- fload the communications and let the processing elements play their strengths on the computation

Canary: Congestion-Aware In-Network Allreduce Using Dynamic Trees

The allreduce operation is an essential building block for many distributed applications, ranging from the training of deep learning models to scientific computing. In an allreduce operation, data from multiple hosts is aggregated together and then broadcasted to each host participating in the operation. Allreduce performance can be improved by a factor of two by aggregating the data directly in the network. Switches aggregate data coming from multiple ports before forwarding the partially aggregated result to the next hop. In all existing solutions, each switch needs to know the ports from which it will receive the data to aggregate. However, this forces packets to traverse a predefined set of switches, making these solutions prone to congestion. For this reason, we design Canary, the first congestion-aware in-network allreduce algorithm. Canary uses load balancing algorithms to forward packets on the least congested paths. Because switches do not know from which ports they will receive the data to aggregate, they use timeouts to aggregate the data in a best-effort way. We develop a P4 Canary prototype and evaluate it on a Tofino switch. We then validate Canary through simulations on large networks, showing performance improvements up to 40% compared to the state-of-the-art

Routing on the Channel Dependency Graph:: A New Approach to Deadlock-Free, Destination-Based, High-Performance Routing for Lossless Interconnection Networks

In the pursuit for ever-increasing compute power, and with Moore's law slowly coming to an end, high-performance computing started to scale-out to larger systems. Alongside the increasing system size, the interconnection network is growing to accommodate and connect tens of thousands of compute nodes. These networks have a large influence on total cost, application performance, energy consumption, and overall system efficiency of the supercomputer. Unfortunately, state-of-the-art routing algorithms, which define the packet paths through the network, do not utilize this important resource efficiently. Topology-aware routing algorithms become increasingly inapplicable, due to irregular topologies, which either are irregular by design, or most often a result of hardware failures. Exchanging faulty network components potentially requires whole system downtime further increasing the cost of the failure. This management approach becomes more and more impractical due to the scale of today's networks and the accompanying steady decrease of the mean time between failures. Alternative methods of operating and maintaining these high-performance interconnects, both in terms of hardware- and software-management, are necessary to mitigate negative effects experienced by scientific applications executed on the supercomputer. However, existing topology-agnostic routing algorithms either suffer from poor load balancing or are not bounded in the number of virtual channels needed to resolve deadlocks in the routing tables. Using the fail-in-place strategy, a well-established method for storage systems to repair only critical component failures, is a feasible solution for current and future HPC interconnects as well as other large-scale installations such as data center networks. Although, an appropriate combination of topology and routing algorithm is required to minimize the throughput degradation for the entire system. This thesis contributes a network simulation toolchain to facilitate the process of finding a suitable combination, either during system design or while it is in operation. On top of this foundation, a key contribution is a novel scheduling-aware routing, which reduces fault-induced throughput degradation while improving overall network utilization. The scheduling-aware routing performs frequent property preserving routing updates to optimize the path balancing for simultaneously running batch jobs. The increased deployment of lossless interconnection networks, in conjunction with fail-in-place modes of operation and topology-agnostic, scheduling-aware routing algorithms, necessitates new solutions to solve the routing-deadlock problem. Therefore, this thesis further advances the state-of-the-art by introducing a novel concept of routing on the channel dependency graph, which allows the design of an universally applicable destination-based routing capable of optimizing the path balancing without exceeding a given number of virtual channels, which are a common hardware limitation. This disruptive innovation enables implicit deadlock-avoidance during path calculation, instead of solving both problems separately as all previous solutions

Master of Science

thesisIntegrated circuits often consist of multiple processing elements that are regularly tiled across the two-dimensional surface of a die. This work presents the design and integration of high speed relative timed routers for asynchronous network-on-chip. It researches NoC's efficiency through simplicity by directly translating simple T-router, source-routing, single-flit packet to higher radix routers. This work is intended to study performance and power trade-offs adding higher radix routers, 3D topologies, Virtual Channels, Accurate NoC modeling, and Transmission line communication links. Routers with and without virtual channels are designed and integrated to arrayed communication networks. Furthermore, the work investigates 3D networks with diffusive RC wires and transmission lines on long wrap interconnects

UNIFICO: Thread Migration in Heterogeneous-ISA CPUs without State Transformation

Heterogeneous-ISA processor designs have attracted considerable research interest. However, unlike their homogeneous-ISA counterparts, explicit software support for bridging ISA heterogeneity is required. The lack of a compilation toolchain ready to support heterogeneous-ISA targets has been a major factor hindering research in this exciting emerging area. For any such compiler “getting right” the mechanics involved in state transformation upon migration and doing this efficiently is of critical importance. In particular, any runtime conversion of the current program stack from one architecture to another would be prohibitively expensive. In this paper, we design and develop Unifico, a new multi-ISA compiler that generates binaries that maintain the same stack layout during their execution on either architecture. Unifico avoids the need for runtime stack transformation, thus eliminating overheads associated with ISA migration. Additional responsibilities of the Unifico compiler backend include maintenance of a uniform ABI and virtual address space across ISAs. Unifico is implemented using the LLVM compiler infrastructure, and we are currently targeting the x86-64 and ARMv8 ISAs. We have evaluated Unifico across a range of compute-intensive NAS benchmarks and show its minimal impact on overall execution time, where less than 6% overhead is introduced on average. When compared against the state-of-the-art Popcorn compiler, Unifico reduces binary size overhead from ∼200% to ∼10%, whilst eliminating the stack transformation overhead during ISA migration

Light-Weight Remote Communication for High-Performance Cloud Networks

Während der letzten 10 Jahre gewann das Cloud Computing immer weiter an Bedeutung. Um kosten zu sparen installieren immer mehr Anwender ihre Anwendungen in der Cloud, statt eigene Hardware zu kaufen und zu betreiben. Als Reaktion entstanden große Rechenzentren, die ihren Kunden Rechnerkapazität zum Betreiben eigener Anwendungen zu günstigen Preisen anbieten. Diese Rechenzentren verwenden momentan gewöhnliche Rechnerhardware, die zwar leistungsstark ist, aber hohe Anschaffungs- und Stromkosten verursacht. Aus diesem Grund werden momentan neue Hardwarearchitekturen mit schwächeren aber energieeffizienteren CPUs entwickelt. Wir glauben, dass in zukünftiger Cloudhardware außerdem Netzwerkhardware mit Zusatzfunktionen wie user-level I/O oder remote DMA zum Einsatz kommt, um die CPUs zu entlasten. Aktuelle Cloud-Plattformen setzen meist bekannte Betriebssysteme wie Linux oder Microsoft Windows ein, um Kompatibilität mit existierender Software zu gewährleisten. Diese Betriebssysteme beinhalten oft keine Unterstützung für die speziellen Funktionen zukünftiger Netzwerkhardware. Stattdessen verwenden sie traditionell software-basierte Netzwerkstacks, die auf TCP/IP und dem Berkeley-Socket-Interface basieren. Besonders das Socket-Interface ist mit Funktionen wie remote DMA weitgehend inkompatibel, da seine Semantik auf Datenströmen basiert, während remote DMA-Anfragen sich eher wie in sich abgeschlossene Nachrichten verhalten. In der vorliegenden Arbeit beschreiben wir LibRIPC, eine leichtgewichtige Kommunikationsbibliothek für Cloud-Anwendungen. LibRIPC verbessert die Leistung zukünftiger Netzwerkhardware signifikant, ohne dabei die von Anwendungen benötigte Flexibilität zu vernachlässigen. Anstatt Sockets bietet LibRIPC eine nachrichtenbasierte Schnittstelle an, zwei Funktionen zum senden von Daten implementiert: Eine Funktion für kurze Nachrichten, die auf niedrige Latenz optimiert ist, sowie eine Funktion für lange Nachrichten, die durch die Nutzung von remote DMA-Funktionalität hohe Datendurchsätze erreicht. Übertragene Daten werden weder beim Senden noch beim Empfangen kopiert, um die Übertragungslatenz zu minimieren. LibRIPC nutzt den vollen Funktionsumfang der Hardware aus, versteckt die Hardwarefunktionen aber gleichzeitig vor der Anwendung, um die Hardwareunabhängigkeit der Anwendung zu gewährleisten. Um Flexibilität zu erreichen verwendet die Bibliothek ein eigenes Adressschema, dass sowohl von der verwendeten Hardware als auch von physischen Maschinen unabhängig ist. Hardwareabhängige Adressen werden dynamisch zur Laufzeit aufgelöst, was starten, stoppen und migrieren von Prozessen zu beliebigen Zeitpunkten erlaubt. Um unsere Lösung zu Bewerten implementierten wir einen Prototypen auf Basis von InfiniBand. Dieser Prototyp nutzt die Vorteile von InfiniBand, um effiziente Datenübertragungen zu ermöglichen, und vermeidet gleichzeitig die Nachteile von InfiniBand, indem er die Ergebnisse langwieriger Operationen speichert und wiederverwendet. Wir führten Experimente auf Basis dieses Prototypen und des Webservers Jetty durch. Zu diesem Zweck integrierten wir Jetty in das Hadoop map/reduce framework, um realistische Lastbedingungen zu erzeugen. Während dabei die effiziente Integration von LibRIPC und Jetty vergleichsweise einfach war, erwies sich die Integration von LibRIPC und Hadoop als deutlich schwieriger: Um unnötiges Kopieren von Daten zu vermeiden, währen weitgehende Änderungen an der Codebasis von Hadoop erforderlich. Dennoch legen unsere Ergebnisse nahe, dass LibRIPC Datendurchsatz, Latenz und Overhead gegenüber Socketbasierter Kommunikation deutlich verbessert

LDCR: A Novel Cloud Routing Mechanism for Increasing Network Throughput

Aiming at the limitations of existing solutions for network bottleneck caused by the gap between the network transmission capacity and the processing capability of the current Internet architecture, this paper presents a novel cloud routing protocol named LD-CR based on limited deflection mechanism. A router running this protocol logically separates data forwarding and processing, which could share cloud processing resources with other nodes by using extra bandwidth. This method could effectively alleviate single-node congestion by leverage of the capability of electronic processing and optical transmission capacity at the same node. Analysis and simulation results show the simple LD-CR protocol could improve network performance in packet loss rate and network throughput obviously compared with OSPF protocol. Meanwhile, this method could also avoid performance rapid deterioration under the condition of heavy traffic load by using traditional deflection routing technology

HyperFPGA: SoC-FPGA Cluster Architecture for Supercomputing and Scientific applications

Since their inception, supercomputers have addressed problems that far exceed those of a single computing device. Modern supercomputers are made up of tens of thousands of CPUs and GPUs in racks that are interconnected via elaborate and most of the time ad hoc networks. These large facilities provide scientists with unprecedented and ever-growing computing power capable of tackling more complex and larger problems. In recent years, the most powerful supercomputers have already reached megawatt power consumption levels, an important issue that challenges sustainability and shows the impossibility of maintaining this trend. With more pressure on energy efficiency, an alternative to traditional architectures is needed. Reconfigurable hardware, such as FPGAs, has repeatedly been shown to offer substantial advantages over the traditional supercomputing approach with respect to performance and power consumption. In fact, several works that advanced the field of heterogeneous supercomputing using FPGAs are described in this thesis \cite{survey-2002}. Each cluster and its architectural characteristics can be studied from three interconnected domains: network, hardware, and software tools, resulting in intertwined challenges that designers must take into account. The classification and study of the architectures illustrate the trade-offs of the solutions and help identify open problems and research lines, which in turn served as inspiration and background for the HyperFPGA. In this thesis, the HyperFPGA cluster is presented as a way to build scalable SoC-FPGA platforms to explore new architectures for improved performance and energy efficiency in high-performance computing, focusing on flexibility and openness. The HyperFPGA is a modular platform based on a SoM that includes power monitoring tools with high-speed general-purpose interconnects to offer a great level of flexibility and introspection. By exploiting the reconfigurability and programmability offered by the HyperFPGA infrastructure, which combines FPGAs and CPUs, with high-speed general-purpose connectors, novel computing paradigms can be implemented. A custom Linux OS and drivers, along with a custom script for hardware definition, provide a uniform interface from application to platform for a programmable framework that integrates existing tools. The development environment is demonstrated using the N-Queens problem, which is a classic benchmark for evaluating the performance of parallel computing systems. Overall, the results of the HyperFPGA using the N-Queens problem highlight the platform's ability to handle computationally intensive tasks and demonstrate its suitability for its use in supercomputing experiments.Since their inception, supercomputers have addressed problems that far exceed those of a single computing device. Modern supercomputers are made up of tens of thousands of CPUs and GPUs in racks that are interconnected via elaborate and most of the time ad hoc networks. These large facilities provide scientists with unprecedented and ever-growing computing power capable of tackling more complex and larger problems. In recent years, the most powerful supercomputers have already reached megawatt power consumption levels, an important issue that challenges sustainability and shows the impossibility of maintaining this trend. With more pressure on energy efficiency, an alternative to traditional architectures is needed. Reconfigurable hardware, such as FPGAs, has repeatedly been shown to offer substantial advantages over the traditional supercomputing approach with respect to performance and power consumption. In fact, several works that advanced the field of heterogeneous supercomputing using FPGAs are described in this thesis \cite{survey-2002}. Each cluster and its architectural characteristics can be studied from three interconnected domains: network, hardware, and software tools, resulting in intertwined challenges that designers must take into account. The classification and study of the architectures illustrate the trade-offs of the solutions and help identify open problems and research lines, which in turn served as inspiration and background for the HyperFPGA. In this thesis, the HyperFPGA cluster is presented as a way to build scalable SoC-FPGA platforms to explore new architectures for improved performance and energy efficiency in high-performance computing, focusing on flexibility and openness. The HyperFPGA is a modular platform based on a SoM that includes power monitoring tools with high-speed general-purpose interconnects to offer a great level of flexibility and introspection. By exploiting the reconfigurability and programmability offered by the HyperFPGA infrastructure, which combines FPGAs and CPUs, with high-speed general-purpose connectors, novel computing paradigms can be implemented. A custom Linux OS and drivers, along with a custom script for hardware definition, provide a uniform interface from application to platform for a programmable framework that integrates existing tools. The development environment is demonstrated using the N-Queens problem, which is a classic benchmark for evaluating the performance of parallel computing systems. Overall, the results of the HyperFPGA using the N-Queens problem highlight the platform's ability to handle computationally intensive tasks and demonstrate its suitability for its use in supercomputing experiments

The effect of an optical network on-chip on the performance of chip multiprocessors

Optical networks on-chip (ONoC) have been proposed to reduce power consumption and increase bandwidth density in high performance chip multiprocessors (CMP), compared to electrical NoCs. However, as buffering in an ONoC is not viable, the end-to-end message path needs to be acquired in advance during which the message is buffered at the network ingress. This waiting latency is therefore a combination of path setup latency and contention and forms a significant part of the total message latency. Many proposed ONoCs, such as Single Writer, Multiple Reader (SWMR), avoid path setup latency at the expense of increased optical components. In contrast, this thesis investigates a simple circuit-switched ONoC with lower component count where nodes need to request a channel before transmission. To hide the path setup latency, a coherence-based message predictor is proposed, to setup circuits before message arrival. Firstly, the effect of latency and bandwidth on application performance is thoroughly investigated using full-system simulations of shared memory CMPs. It is shown that the latency of an ideal NoC affects the CMP performance more than the NoC bandwidth. Increasing the number of wavelengths per channel decreases the serialisation latency and improves the performance of both ONoC types. With 2 or more wavelengths modulating at 25 Gbit=s , the ONoCs will outperform a conventional electrical mesh (maximal speedup of 20%). The SWMR ONoC outperforms the circuit-switched ONoC. Next coherence-based prediction techniques are proposed to reduce the waiting latency. The ideal coherence-based predictor reduces the waiting latency by 42%. A more streamlined predictor (smaller than a L1 cache) reduces the waiting latency by 31%. Without prediction, the message latency in the circuit-switched ONoC is 11% larger than in the SWMR ONoC. Applying the realistic predictor reverses this: the message latency in the SWMR ONoC is now 18% larger than the predictive circuitswitched ONoC