Power-efficient DRAM Speculation (PEDS) is a power optimization targeted at broadcast-based shared-memory multiprocessor systems that speculatively access DRAM in parallel with the broadcast snoop. Although speculatively accessing DRAM has the potential performance advantage of overlapping DRAM latency with the snoop, it wastes power for memory requests that obtain data from other processorspsila caches. PEDS takes advantage of information provided by a Region Coherence Array to identify requests that have a high likelihood of obtaining data from another processorpsilas cache, and does not access DRAM speculatively for those requests. By doing so, PEDS eliminates DRAM reads, reduces DRAM power consumption, reduces contention for DRAM resources, and increases the opportunity for DRAM power management. PEDS requires almost no additional hardware in systems that incorporate Region Coherence Arrays. Detailed simulation results show PEDS reduces average DRAM read traffic 28-32%, reduces average DRAM power dissipation 17-22%, and reduces average DRAM energy consumption 16-21%.

High availability systems typically rely on redundant components and functionality to achieve fault detection, isolation and fail over. In the future, increases in error rates will make high availability important even in the commodity and volume market. Systems will be built out of chip multiprocessors (CMPs) with multiple identical components that can be configured to provide redundancy for high availability. However, the 100% overhead of making all components redundant is going to be unacceptable for the commodity market, especially when all applications might not require high availability. In particular, duplicating the entire memory like the current high availability systems (e.g. NonStop and Stratus) do is particularly problematic given the fact that system costs are going to be dominated by the cost of memory. In this paper, we propose a novel technique called a duplication cache to reduce the overhead of memory duplication in CMP-based high availability systems. A duplication cache is a reserved area of main memory that holds copies of pages belonging to the current write working set (set of actively modified pages) of running processes. All other pages are marked as read-only and are kept only as a single, shared copy. The size of the duplication cache can be configured dynamically at runtime and allows system designers to trade off the cost of memory duplication with minor performance overhead. We extensively analyze the effectiveness of our duplication cache technique and show that for a range of benchmarks memory duplication can be reduced by 60-90% with performance degradation ranging from 1-12%. On average, a duplication cache can reduce memory duplication by 60% for a performance overhead of 4% and by 90% for a performance overhead of 5%.

High availability is an increasingly important requirement for enterprise systems, often valued more than performance. Systems designed for high availability typically use redundant hardware for error detection and continued uptime in the event of a failure. Chip multiprocessors with an abundance of identical resources like cores, cache and interconnection networks would appear to be ideal building blocks for implementing high availability solutions on chip. However, doing so poses significant challenges with respect to error containment and faulty component replacement. Increasing silicon and transient fault rates with future technology scaling exacerbate the problem. This paper proposes a novel, cost-effective, architecture for high availability systems built from future multi-core processors. We propose a new chip multiprocessor architecture that provides configurable isolation for fault containment and component retirement, based upon cost-effective modifications to commodity designs. The design is evaluated for a state-of-the-art industrial fault model and the proposed architecture is shown to provide effective fault isolation and graceful degradation even when the failure rate is high.

We propose and evaluate a multi-thread memory scheduler that targets high performance CMPs. The proposed memory scheduler is based on concepts originally developed for network fair queuing scheduling algorithms. The memory scheduler is fair and provides quality of service (QoS) while improving system performance. On a four processor CMP running workloads containing a mix of applications with a range of memory bandwidth demands, the proposed memory scheduler provides QoS to all of the threads in all of the workloads, improves system performance by an average of 14% (41% in the best case), and reduces the variance in the threads' target memory bandwidth utilization from .2 to .0058