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As we enter the era of CMP platforms with multiple threads/cores on the die, the diversity of the simultaneous workloads running on them is expected to increase. The rapid deployment of virtualization as a means to consolidate workloads on to a single platform is a prime example of this trend. In such scenarios, the quality of service (QoS) that each individual workload gets from the platform can widely vary depending on the behavior of the simultaneously running workloads. While the number of cores assigned to each workload can be controlled, there is no hardware or software support in today's platforms to control allocation of platform resources such as cache space and memory bandwidth to individual workloads. In this paper, we propose a QoS-enabled memory architecture for CMP platforms that addresses this problem. The QoS-enabled memory architecture enables more cache resources (i.e. space) and memory resources (i.e. bandwidth) for high priority applications based on guidance from the operating environment. The architecture also allows dynamic resource reassignment during run-time to further optimize the performance of the high priority application with minimal degradation to low priority. To achieve these goals, we will describe the hardware/software support required in the platform as well as the operating environment (O/S and virtual machine monitor). Our evaluation framework consists of detailed platform simulation models and a QoS-enabled version of Linux. Based on evaluation experiments, we show the effectiveness of a QoSenabled architecture and summarize key findings/trade-offs.

As chip multiprocessors (CMPs) become increasingly mainstream, architects have likewise become more interested in how best to share a cache hierarchy among multiple simultaneous threads of execution. The complexity of this problem is exacerbated as the number of simultaneous threads grows from two or four to the tens or hundreds. However, there is no consensus in the architectural community on what "best" means in this context. Some papers in the literature seek to equalize each thread's performance loss due to sharing, while others emphasize maximizing overall system performance. Furthermore, the specific effect of these goals varies depending on the metric used to define "performance".In this paper we label equal performance targets as Communist cache policies and overall performance targets as Utilitarian cache policies. We compare both of these models to the most common current model of a free-for-all cache (a Capitalist policy). We consider various performance metrics, including miss rates, bandwidth usage, and IPC, including both absolute and relative values of each metric. Using analytical models and behavioral cache simulation, we find that the optimal partitioning of a shared cache can vary greatly as different but reasonable definitions of optimality are applied. We also find that, although Communist and Utilitarian targets are generally compatible, each policy has workloads for which it provides poor overall performance or poor fairness, respectively. Finally, we find that simple policies like LRU replacement and static uniform partitioning are not sufficient to provide near-optimal performance under any reasonable definition, indicating that some thread-aware cache resource allocation mechanism is required.

With the advent of dual-core chips in the marketplace, small-scale CMP (chip multiprocessor) architectures are becoming commonplace. We expect a continuing trend of increasing the number of cores on a die to maximize the performance/power efficiency of a single chip. We believe an era of large-scale CMPs (LCMPs) with several tens to hundreds of cores is on the way, but as of now architects have little understanding of how best to build a cache hierarchy given such a large number of cores/threads to support. With this in mind, our initial goals are to prune the cache design space for LCMPs by characterizing basic server workload behavior in such an environment.In this paper, we describe the range of methodologies that we are developing to overcome the challenges of exploring the cache design space for LCMP platforms. We then focus on employing a trace-driven approach to characterizing one key server workload (OLTP) in both a homogeneous and a heterogeneous workload environment. We study the effect of increasing threads (from 1 to 128) on a three-level cache hierarchy with emphasis on second and third level caches. We study the effect of varying sizes at these cache levels and show the effects of threads contending for cache space, the effects of prefetching instruction addresses, and the effects of inclusion. We make initial observations and conclusions about the factors on which LCMP cache hierarchy design decisions should be based and discuss future work.

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