Networks-on-Chips (NoCs) are experiencing escalating susceptibility to wear-out and reduced reliability, with the risk of becoming the key point of failure in an entire multicore chip. Aiming towards seamless NoC operation in the presence of faulty communication links, in this paper we propose Hermes, a highly-robust, distributed and lightweight fault-tolerant routing algorithm, whose performance degrades gracefully with increasing faulty link counts. Hermes is a deadlock-free hybrid routing algorithm, utilizing load-balancing routing on fault-free paths to sustain high-performance, while providing pre-reconfigured escape path selection in the vicinity of faults. Additionally, Hermes identifies non-communicating network partitions in scenarios where faulty links are topologically densely distributed. An extensive experimental evaluation, including utilizing traffic benchmarks gathered from full-system chip multi-processor simulations, shows that Hermes improves network throughput by up to 3× when compared against prior-art.

Networks-on-Chips (NoCs) are experiencing escalating susceptibility to wear-out and reduced reliability, with the risk of becoming the key point of failure in an entire multicore chip. In this paper we propose Hermes, a highly-robust, distributed fault-tolerant routing algorithm, whose performance degrades gracefully with increasing faulty NoC link counts. Hermes is a deadlock-free hybrid routing algorithm, utilizing load-balanced routing on fault-free paths, while providing pre-reconfigured escape routes in the vicinity of faults. An initial experimental evaluation shows that Hermes improves network throughput by up to 2.2× when compared against the existing state-of-the-art.

Chip Multi-Processors (CMPs) are designed with an increasing number of cores to enable multiple and potentially heterogeneous applications to run simultaneously on the same system. However, this results in increasing pressure on shared resources, such as shared caches. With multiple processor cores sharing the same caches, high-priority applications may end up contending with low-priority applications for cache space and suffer significant performance slow-down, hence affecting the Quality of Service (QoS). In datacenters, Service Level Agreements (SLAs) impose a reserved amount of computing resources and specific cache space per cloud customer. Thus, to meet SLAs, a deterministic capacity management solution is required to control the occupancy of all applications. In this paper, we propose a novel QoS architecture, based on Probabilistic Selective Allocation (PSA), for priority-aware caches. Further, we show that applying a control-theoretic approach (Proportional Integral controller) to dynamically adjust PSA provides accurate and fine-grained capacity management.

Extreme transistor technology scaling is causing increasing concerns in device reliability: the expected lifetime of individual transistors in complex chips is quickly decreasing, and the problem is expected to worsen at future technology nodes. With complex designs increasingly relying on Networks-on-Chip (NoCs) for on-chip data transfers, a NoC must continue to operate even in the face of many transistor failures. Specifically, it must be able to reconfigure and reroute packets around faults to enable continued operation, i.e., generate new routing paths to replace the old ones upon a failure. In addition to these reliability requirements, NoCs must maintain low latency and high throughput at very low area budget. In this work, we propose a distributed reconfiguration solution named Ariadne, targeting large, aggressively scaled, unreliable NoCs. Ariadne utilizes up*/down* for fast routing at high bandwidth, and upon any number of concurrent network failures in any location, it reconfigures to discover new resilient paths to connect the surviving nodes. Experimental results show that Ariadne provides a 40%-140% latency improvement (when subject to 50 faults in a 64-node NoC) over other on-chip state-of-the-art fault tolerant solutions, while meeting the low area budget of on-chip routers with an overhead of just 1.97%.

Process Variation (PV) is increasingly threatening the reliability of Networks-on-Chips. Thus, various resilient router designs have been recently proposed and evaluated. However, these evaluations assume random fault distributions, which result in 52%--81% inaccuracy. We propose an accurate circuit-level fault-modeling tool, which can be plugged into any system-level NoC simulator, quantify the system-level impact of PV-induced faults at runtime, pinpoint fault-prone router components that should be protected, and accurately evaluate alternative resilient multi-core designs.

As transistor dimensions continue to scale deep into the nanometer regime, silicon reliability is becoming a chief concern. At the same time, transistor counts are scaling up, enabling the design of highly integrated chips with many cores and a complex interconnect fabric, often a network on chip (NoC). Particularly problematic is the case when the accumulation of permanent hardware faults leads to disconnected cores in the system. In order to maintain correct system operation, it is necessary to salvage the data from these isolated nodes.

Many data centers employ server consolidation to maximize the efficiency of platform resource usage. As a result, multiple virtual machines (VMs) simultaneously run on each data center platform. Contention for shared resources between these virtual machines has an undesirable and non-deterministic impact on their performance behavior in such platforms. This paper proposes the use of shared resource monitoring to (a) understand the resource usage of each virtual machine on each platform, (b) collect resource usage and performance across different platforms to correlate implications of usage to performance, and (c) migrate VMs that are resource-constrained to improve overall data center throughput and improve Quality of Service (QoS). We focus our efforts on monitoring and addressing shared cache contention and propose a new optimization metric that captures the priority of the VM and the overall weighted throughput of the data center. We conduct detailed experiments emulating data center scenarios including on-line transaction processing workloads (based on TPC-C) middle-tier workloads (based on SPECjbb and SPECjAppServer) and financial workloads (based on PARSEC). We show that monitoring shared resource contention (such as shared cache) is highly beneficial to better manage throughput and QoS in a cloud-computing data center environment.

Aggressive transistor scaling continues to increase integration capacity with each new technology node, but technology downscaling also increases the vulnerability of semiconductor devices and causes silicon failures. Thus, fault-tolerant architectures are emerging to guarantee reliable functionality on unreliable silicon. While tolerating faults within a processor core has been extensively researched, the many-core era introduces the challenge of reliable on-chip communication in Chip Multi-Processors (CMPs). In CMP systems, an unreliable interconnection network can lose or corrupt coherence messages, causing the entire chip to deadlock. In this work, we argue for a system-level resiliency solution to tolerate an unreliable underlying Network-on-Chip (NoC). We introduce a systematic methodology to transform a coherence protocol to a resilient one, by extending its Finite State Machine (FSM) with safe states and incorporating additional handshaking messages into transactions. The modified protocol ensures coherent and reliable transactions over any lossy NoC. Our approach is generic and can be applied to a wide range of protocols. It requires minimal hardware modifications and introduces only a slight performance overhead (an average of 0.8% during fault-free operation, and 1.9% even at an aggressive fault rate of one fault per msec).

CMPs have now become mainstream and are growing in complexity with more cores, several shared resources (cache, memory, etc) and the potential for additional heterogeneous elements. In order to manage these resources, it is becoming critical to optimize the interaction between the execution environment (operating systems, virtual machine monitors, etc) and the CMP platform. Performance analysis of such OS and CMP interactions is challenging because it requires long running full-system execution-driven simulations. In this paper, we explore an alternative approach (CMPSched$im) to evaluate the interaction of OS and CMP architectures. In particular, CMPSched$im is focused on evaluating techniques to address the shared cache management problem through better interaction between CMP hardware and operating system scheduling. CMPSched$im enables fast and flexible exploration of this interaction by combining the benefits of (a) binary instrumentation tools (Pin), (b) user-level scheduling tools (Linsched) and (c) simple core/cache simulators. In this paper, we describe CMPSched$im in detail and present case studies showing how CMPSched$im can be used to optimize OS scheduling by taking advantage of novel shared cache monitoring capabilities in the hardware. We also describe OS scheduling heuristics to improve overall system performance through resource monitoring and application classification to achieve near optimal scheduling that minimizes the effects of contention in the shared cache of a CMP platform.