This paper investigates the problem of partitioning a shared cache between multiple concurrently executing applications. The commonly used LRU policy implicitly partitions a shared cache on a demand basis, giving more cache resources to the application that has a high demand and fewer cache resources to the application that has a low demand. However, a higher demand for cache resources does not always correlate with a higher performance from additional cache resources. It is beneficial for performance to invest cache resources in the application that benefits more from the cache resources rather than in the application that has more demand for the cache resources. This paper proposes utility-based cache partitioning (UCP), a low-overhead, runtime mechanism that partitions a shared cache between multiple applications depending on the reduction in cache misses that each application is likely to obtain for a given amount of cache resources. The proposed mechanism monitors each application at runtime using a novel, cost-effective, hardware circuit that requires less than 2kB of storage. The information collected by the monitoring circuits is used by a partitioning algorithm to decide the amount of cache resources allocated to each application. Our evaluation, with 20 multiprogrammed workloads, shows that UCP improves performance of a dual-core system by up to 23% and on average 11% over LRU-based cache partitioning.

/pdf/utility-based-cache-partitioning-a-low-overhead-high-4t50s5xud2.pdf

Utility-Based Cache Partitioning: A Low-Overhead, High-Performance, Runtime Mechanism to Partition Shared Caches

Chip-level power and thermal implications will continue to rule as one of the primary design constraints and performance limiters. The gap between average and peak power actually widens with increased levels of core integration. As such, if per-core control of power levels (modes) is possible, a global power manager should be able to dynamically set the modes suitably. This would be done in tune with the workload characteristics, in order to always maintain a chip-level power that is below the specified budget. Furthermore, this should be possible without significant degradation of chip-level throughput performance. We analyze and validate this concept in detail in this paper. We assume a per-core DVFS (dynamic voltage and frequency scaling) knob to be available to such a conceptual global power manager. We evaluate several different policies for global multi-core power management. In this analysis, we consider various different objectives such as prioritization and optimized throughput. Overall, our results show that in the context of a workload comprised of SPEC benchmark threads, our best architected policies can come within 1% of the performance of an ideal oracle, while meeting a given chip-level power budget. Furthermore, we show that these global dynamic management policies perform significantly better than static management, even if static scheduling is given oracular knowledge.

/pdf/an-analysis-of-efficient-multi-core-global-power-management-1ni9trrk1b.pdf

An Analysis of Efficient Multi-Core Global Power Management Policies: Maximizing Performance for a Given Power Budget

DRAM memory is a major resource shared among cores in a chip multiprocessor (CMP) system. Memory requests from different threads can interfere with each other. Existing memory access scheduling techniques try to optimize the overall data throughput obtained from the DRAM and thus do not take into account inter-thread interference. Therefore, different threads running together on the same chip can ex- perience extremely different memory system performance: one thread can experience a severe slowdown or starvation while another is un- fairly prioritized by the memory scheduler. This paper proposes a new memory access scheduler, called the Stall-Time Fair Memory scheduler (STFM), that provides quality of service to different threads sharing the DRAM memory system. The goal of the proposed scheduler is to "equalize" the DRAM-related slowdown experienced by each thread due to interference from other threads, without hurting overall system performance. As such, STFM takes into account inherent memory characteristics of each thread and does not unfairly penalize threads that use the DRAM system without interfering with other threads. We show that STFM significantly reduces the unfairness in the DRAM system while also improving system throughput (i.e., weighted speedup of threads) on a wide variety of workloads and systems. For example, averaged over 32 different workloads running on an 8-core CMP, the ratio between the highest DRAM-related slowdown and the lowest DRAM-related slowdown reduces from 5.26X to 1.4X, while the average system throughput improves by 7.6%. We qualitatively and quantitatively compare STFM to one new and three previously- proposed memory access scheduling algorithms, including network fair queueing. Our results show that STFM provides the best fairness, system throughput, and scalability.

/pdf/stall-time-fair-memory-access-scheduling-for-chip-kj13rlydtp.pdf

Stall-Time Fair Memory Access Scheduling for Chip Multiprocessors

In a chip-multiprocessor (CMP) system, the DRAM system isshared among cores. In a shared DRAM system, requests from athread can not only delay requests from other threads by causingbank/bus/row-buffer conflicts but they can also destroy other threads’DRAM-bank-level parallelism. Requests whose latencies would otherwisehave been overlapped could effectively become serialized. As aresult both fairness and system throughput degrade, and some threadscan starve for long time periods.This paper proposes a fundamentally new approach to designinga shared DRAM controller that provides quality of service to threads,while also improving system throughput. Our parallelism-aware batchscheduler (PAR-BS) design is based on two key ideas. First, PARBSprocesses DRAM requests in batches to provide fairness and toavoid starvation of requests. Second, to optimize system throughput,PAR-BS employs a parallelism-aware DRAM scheduling policythat aims to process requests from a thread in parallel in the DRAMbanks, thereby reducing the memory-related stall-time experienced bythe thread. PAR-BS seamlessly incorporates support for system-levelthread priorities and can provide different service levels, includingpurely opportunistic service, to threads with different priorities.We evaluate the design trade-offs involved in PAR-BS and compareit to four previously proposed DRAM scheduler designs on 4-, 8-, and16-core systems. Our evaluations show that, averaged over 100 4-coreworkloads, PAR-BS improves fairness by 1.11X and system throughputby 8.3% compared to the best previous scheduling technique, Stall-Time Fair Memory (STFM) scheduling. Based on simple request prioritizationrules, PAR-BS is also simpler to implement than STFM.

http://users.ece.cmu.edu/~omutlu/pub/parbs_isca08.pdf

Parallelism-Aware Batch Scheduling: Enhancing both Performance and Fairness of Shared DRAM Systems

This paper presents a detailed study of fairness in cache sharing between threads in a chip multiprocessor (CMP) architecture. Prior work in CMP architectures has only studied throughput optimization techniques for a shared cache. The issue of fairness in cache sharing, and its relation to throughput, has not been studied. Fairness is a critical issue because the operating system (OS) thread scheduler's effectiveness depends on the hardware to provide fair cache sharing to co-scheduled threads. Without such hardware, serious problems, such as thread starvation and priority inversion, can arise and render the OS scheduler ineffective. This paper makes several contributions. First, it proposes and evaluates five cache fairness metrics that measure the degree of fairness in cache sharing, and shows that two of them correlate very strongly with the execution-time fairness. Execution-time fairness is defined as how uniform the execution times of co-scheduled threads are changed, where each change is relative to the execution time of the same thread running alone. Secondly, using the metrics, the paper proposes static and dynamic L2 cache partitioning algorithms that optimize fairness. The dynamic partitioning algorithm is easy to implement, requires little or no profiling, has low overhead, and does not restrict the cache replacement algorithm to LRU. The static algorithm, although requiring the cache to maintain LRU stack information, can help the OS thread scheduler to avoid cache thrashing. Finally, this paper studies the relationship between fairness and throughput in detail. We found that optimizing fairness usually increases throughput, while maximizing throughput does not necessarily improve fairness. Using a set of co-scheduled pairs of benchmarks, on average our algorithms improve fairness by a factor of 4/spl times/, while increasing the throughput by 15%, compared to a nonpartitioned shared cache.

/pdf/fair-cache-sharing-and-partitioning-in-a-chip-multiprocessor-16bmv7sfbi.pdf

Fair Cache Sharing and Partitioning in a Chip Multiprocessor Architecture

Simultaneous Multithreading (SMT) is an execution model that executes multiple threads in parallel within a single processor pipeline. Usually, an SMT processor uses shared instruction queues to collect instructions from the different threads. Hence, an SMT processor’s performance depends on how the instruction fetch unit fills these instruction queues every cycle. In the recent past, many schemes have been proposed for fetching instructions into the SMT pipeline. These schemes focussed on increasing the throughput by using the number of instructions and the number of low confidence branch predictions currently in the pipeline, to decide which threads to fetch from. The goal of this paper is to investigate fetch policies that find a balance between fairness and throughput. We present metrics to quantify fairness. We then discuss techniques to use a set of pipeline system variables for achieving balanced throughput and fairness. Finally, we evaluate several fetch policies. Our evaluation confirms that many of our fetch policies provide a good balance between throughput and fairness.

J. Gummaraju

Papers

Balancing thoughput and fairness in SMT processors