A key goodness metric of multi-threaded programs is how their execution times scale when increasing the number of threads. However, there are several bottlenecks that can limit the scalability of a multi-threaded program, e.g., contention for shared cache capacity and off-chip memory bandwidth; and synchronization overheads. In order to improve the scalability of a multi-threaded program, it is vital to be able to quantify how the program is impacted by these scalability bottlenecks. We present a software profiling method for obtaining speedup stacks. A speedup stack reports how much each scalability bottleneck limits the scalability of a multi-threaded program. It thereby quantifies how much its scalability can be improved by eliminating a given bottleneck. A software developer can use this information to determine what optimizations are most likely to improve scalability, while a computer architect can use it to analyze the resource demands of emerging workloads. The proposed method profiles the program on real commodity multi-cores (i.e., no simulations required) using existing performance counters. Consequently, the obtained speedup stacks accurately account for all idiosyncrasies of the machine on which the program is profiled. While the main contribution of this paper is the profiling method to obtain speedup stacks, we present several examples of how speedup stacks can be used to analyze the resource requirements of multi-threaded programs. Furthermore, we discuss how their scalability can be improved by both software developers and computer architects.

Simulators are widely used in computer architecture research. While detailed cycle-accurate simulations provide useful insights, studies using modern workloads typically require days or weeks. Evaluating many design points, only exacerbates the simulation overhead. Recent works propose methods with good accuracy that reduce the simulated overhead either by sampling the execution (e.g., SMARTS and SimPoint) or by using fast analytical models of the simulated designs (e.g., Interval Simulation). While these techniques reduce significantly the simulation overhead, modeling processor components with large state, such as the last-level cache, requires costly simulation to warm them up. Statistical simulation methods, such as SMARTS, report that the warm-up overhead accounts for 99% of the simulation overhead, while only 1% of the time is spent simulating the target design. This paper proposes WarmSim, a method that eliminates the need to warm up the cache. WarmSim builds on top of a statistical cache modeling technique and extends it to model accurately not only the miss ratio but also the outcome of every cache request. WarmSim uses as input, an application's memory reuse information which is hardware independent. Therefore, different cache configurations can be simulated using the same input data. We demonstrate that this approach can be used to estimate the CPI of the SPEC CPU2006 benchmarks with an average error of 1.77%, reducing the overhead compared to a simulation with a 10M instruction warm-up by a factor of 50x.

On multicore processors, co-executing applications compete for shared resources, such as cache capacity and memory bandwidth. This leads to suboptimal resource allocation and can cause substantial performance loss, which makes it important to effectively manage these shared resources. This, however, requires insights into how the applications are impacted by such resource sharing. While there are several methods to analyze the performance impact of cache contention, less attention has been paid to general, quantitative methods for analyzing the impact of contention for memory bandwidth. To this end we introduce the Bandwidth Bandit, a general, quantitative, profiling method for analyzing the performance impact of contention for memory bandwidth on multicore machines. The profiling data captured by the Bandwidth Bandit is presented in a bandwidth graph. This graph accurately captures the measured application's performance as a function of its available memory bandwidth, and enables us to determine how much the application suffers when its available bandwidth is reduced. To demonstrate the value of this data, we present a case study in which we use the bandwidth graph to analyze the performance impact of memory contention when co-running multiple instances of single threaded application.

Applications that are co-scheduled on a multi-core compete for shared resources, such as cache capacity and memory bandwidth. The performance degradation resulting from this contention can be substantial, which makes it important to effectively manage these shared resources. This, however, requires quantitative insight into how applications are impacted by such contention.

Applications that are co-scheduled on a multicore compete for shared resources, such as cache capacity and memory bandwidth. The performance degradation resulting from this contention can be substantial, which makes it important to effectively manage these shared resources. This, however, requires insight into how applications are impacted by such contention. In this paper we present a quantitative method to measure applications' sensitivities to different degrees of contention for off-chip memory bandwidth on real hardware. This method is then used to demonstrate the varying contention sensitivity across a selection of benchmarks, and explains why some of them experience substantial slowdowns long before the overall memory bandwidth saturates.

This work presents StatCC, a simple and efficient model for estimating the shared cache miss ratios of co-scheduled applications on architectures with a hierarchy of private and shared caches. StatCC leverages the StatStack cache model to estimate the co-scheduled applications' cache miss ratios from their individual memory reuse distance distributions, and a simple performance model that estimates their CPIs based on the shared cache miss ratios. These methods are combined into a system of equations that explicitly models the CPIs in terms of the shared miss ratios and can be solved to determine both. The result is a fast algorithm with a 2% error across the SPEC CPU2006 benchmark suite compared to a simulated in-order processor and a hierarchy of private and shared caches.

We present a low-overhead method for accurately measuring application performance (CPI) and off-chip bandwidth (GB/s) as a function of available shared cache capacity. The method is implemented on real hardware, with no modifications to the application or operating system. We accomplish this by co-running a Pirate application that "steals" cache space with the Target application. By adjusting how much space the Pirate steals during the Target's execution, and using hardware performance counters to record the Target's performance, we can accurately and efficiently capture performance data for the Target application as a function of its available shared cache. At the same time we use performance counters to monitor the Pirate to ensure that it is successfully stealing the desired amount of cache. To evaluate this approach, we show that 1) the cache available to the Target behaves as expected, 2) the Pirate steals the desired amount of cache, and ) the Pirate does not bias the Target's performance. As a result, we are able to accurately measure the Target's performance while stealing up to an average of 6.8MB of the 8MB of cache on our Nehalem based test system with an average measurement overhead of only 5.5%.

Chip multiprocessor (CMP) architectures sharing on chip resources, such as last-level caches, have recently become a mainstream computing platform. The performance of such systems can vary greatly depending on how co-scheduled applications compete for these shared resources. This work presents StatCC, a simple and efficient model for estimating the contention for shared cache resources between co-scheduled applications on chip multiprocessor architectures.

Efficient execution on modern architectures requires good data locality, which can be measured by the powerful stack distance abstraction. Based on this abstraction, the miss rate for LRU caches of any size can be predicted. However, measuring stack distance requires the number of unique memory objects to be counted between successive accesses to the same data object, which requires complex and inefficient data collection. This paper presents a new efficient way of estimating the stack distances of an application. Instead of counting the number of unique memory objects touched between successive accesses to the same data, our scheme only requires the number of memory accesses to be counted, a task efficiently handled by existing builtin hardware counters. Furthermore, this information only needs to be captured for a small fraction of the memory accesses. A new efficient off-line algorithm is proposed to estimate the corresponding stack distance based on this sparse information. We evaluate the accuracy of the proposed estimation compared with full stack distance measurements for 28 of the applications in the SPEC CPU2006 benchmark suite. The estimation shows excellent accuracy based on information about only every 10,000th memory access.

Contention for shared cache resources has been recognized as a major bottleneck for multicores--especially for mixed workloads of independent applications. While most modern processors implement instructions to manage caches, these instructions are largely unused due to a lack of understanding of how to best leverage them. This paper introduces a classification of applications into four cache usage categories. We discuss how applications from different categories affect each other's performance indirectly through cache sharing and devise a scheme to optimize such sharing. We also propose a low-overhead method to automatically find the best per-instruction cache management policy. We demonstrate how the indirect cache-sharing effects of mixed workloads can be tamed by automatically altering some instructions to better manage cache resources. Practical experiments demonstrate that our software-only method can improve application performance up to 35% on x86 multicore hardware.