Rapid device aging in the nano era threatens system lifetime reliability, posing a major intrinsic threat to system functionality. Traditional techniques to overcome the aging-induced device slowdown, such as guardbanding are static and incur performance, power, and area penalties. In a manycore processor, the system-level design abstraction offers dynamic opportunities through the control of task-to-core mappings and per-core operation frequency towards more balanced core aging profile across the chip, optimizing the system lifetime reliability while meeting the application performance requirements. This article presents Longevity Framework (LF) that leverages online integrated aging-aware hierarchical mapping and voltage frequency (VF)-selection for lifetime reliability optimization in manycore processors. The mapping exploration is hierarchical to achieve scalability. The VF-selection builds on the trade-offs involved between power, performance, and aging as the VF is scaled while leveraging the per-core DVFS capabilities. The methodology takes the chip-wide process variation into account. Extensive experimentation, comparing the proposed approach with two state-of-the-art methods, for 64-core and 256-core systems running applications from PARSEC and SPLASH-2 benchmark suites, show an improvement of up to 3.2 years in the system lifetime reliability and 4× improvement in the average core health.

Device scaling to subdeca nanometer has pushed device aging as a primary design concern. In manycore systems, inevitable process variation further adds to delay degradation and, coupled with the scalability issues in manycores, makes aging management, while meeting performance demands, a complex problem. LifeGuard is a performance-centric reinforcement learning-based task mapping strategy that leverages the different impact of applications on aging for improving system health. Experimental results, comparing LifeGuard with two state-of-the-art aging optimizing techniques, on a 256-core system, showed that LifeGuard led to improved health for, respectively, 57% and 74% of the cores, and also an enhanced aggregate core frequency.

Aggressive technology scaling enabled very high integration density. Unfortunately, it also led to issues such as process variation, increased power density and consequently rising chip temperature resulting in accelerated device aging and poor lifetime reliability of different components in a manycore system. Moreover, thermal and power limitations let only a fraction of the chip function at full speed; the rest is the dark silicon. Most of the lifetime reliability enhancement solutions for the multi-/manycore systems in the literature are heuristic-based, while some use standard compute-intensive methods to solve the optimization problem making them not scale well with the manycore size. The heuristic-based solutions are formulated to search through the design space of a fine granularity making it huge, limiting their scalability. Also, these approaches do not account for the impact of different applications' execution behavior on the aging of the underlying cores, and their performance requirement distribution across the cores to their advantage. In this paper, we present our resource management strategies towards building scalable lifetime reliability enhancement solutions for dark silicon manycore systems. The first technique, Hierarchical Mapping approach (HiMap), maps a periodic workload employing a block-based hierarchical method that leverages dark cores for thermal mitigation. The second approach, LifeGuard, uses reinforcement learning to learn the applications' aging behavior, and is aware of the performance requirement pattern onto the core frequencies. It maps randomly arriving requests and is scalable to the number of applications and the size of a manycore.

Technology scaling into the nano-scale CMOS regime has resulted in increased leakage and roadblock on voltage scaling, which has led to several issues like high power density and elevated on-chip temperature. This consequently aggravates device aging, compromising lifetime reliability of the manycore systems. This paper proposes HiMap, a dynamic hierarchical mapping approach to maximize lifetime reliability of manycore systems while satisfying performance, power, and thermal constraints. HiMap is process variation- and aging-aware. It comprises of two levels: (1) it identifies a region of cores suitable for mapping, and (2) it maps threads in the region and intersperses dark cores for thermal mitigation while considering the current health of the cores. Both the levels strive to reduce aging variance across the chip. We evaluated HiMap for 64-core and 256-core systems. Results demonstrate an improved system lifetime reliability by up to 2 years at the end of 3.25 years of use, as compared to the state-of-the-art.

Negative bias temperature instability (NBTI) has emerged as a critical challenge to lifetime reliability of computing systems. Traditionally, temperature-aware methodologies are used to mitigate the impact of NBTI on aging and degradation of computing systems. However, in the presence of process variation, which is the norm in manycore processors, temperature-aware techniques are inefficient in improving lifetime reliability and can result in poor performance. In this paper, we propose a novel performance constraint-aware task mapping technique to improve lifetime reliability by mitigating NBTI considering on-chip process variation. Our approach consists of two phases, namely design-time and run-time. During design time, we generate Pareto-optimal mappings. Following which, our run-time technique judiciously intervenes to perform workload migration to save the weakest processing core. We compare our approach with performance-greedy and thermal-aware task mapping techniques. The experiment results demonstrate that our approach outperforms other two techniques and improves lifetime reliability of a manycore system as much as 54% without violating the throughput constraint.

In a cloud computing system, virtual machines owned by different clients are co-hosted on a single physical machine. It is vital to isolate network performance between the clients for ensuring fair usage of the constrained and shared network resources of the physical machine. Unfortunately, the existing network performance isolation techniques are not effective for cloud computing systems because they are difficult to be adopted in a large scale and require non-trivial modification to the network stack of a guest OS. In this paper, we propose a performance isolation-enabled virtual distributed Ethernet (PIE-VDE) to overcome such difficulties. It is a network virtualization software module running on a host OS. It intends to (1) allocate fair share of outgoing link bandwidth to the co-hosted clients and (2) divide a client's share to the virtual machines owned by it in a fair way. Our approach supports full virtualization of a guest OS, ease in wide scale adoption, limited modification to the existing system, low run-time overhead and work-conserving servicing. Experimental results show the effectiveness of the proposed mechanism. Every client received at least 99.5% of its bandwidth share as specified by its weight.