Resource contention profiling collects detailed call stack information each time competing threads in an application are forced to wait for access to Understanding resource contention shared resource. The numbers are shown in terms of the percentage of improvement or the worst-case behavior achieved under DIO relative to that encountered with the default Linux scheduler, so higher bars in this case are better.
Resource contention report views also include timeline graphs that show the individual contention events over time and show the call stacks that created the Understanding resource contention event. We also found that choosing a random schedule produces significantly worse performance, especially as the number of cores Understanding resource contention.
Forecasting timelines The default capabilities forecast a certain number of days into the future based on the number of days for which data has been collected. Although we began this investigation using an analytical modeling approach that would be difficult to implement online, we ultimately arrived at a scheduling method that can be easily implemented online with a modern operating system or even prototyped at the user level.
To estimate the power consumption, we used a rather simplistic model measurements with the actual power meter are still under way but captured the right relationships between power consumed in various load conditions. Figure 2 contrasts the best performance of each application with its worst performance.
On four- and six-core systems, there were such combinations, whereas an eight-core system had 45 combinations, and a ten-core system had only one combination. Cache contention occurs when two or more threads are assigned to run on the cores of the same memory domain for example, Core 0 and Core 1 in figure 1.
Sphinx was paired with Namd, while Soplex ran in the same domain with Gamess. In summary, our investigation of contention-aware scheduling algorithms has taught us that high-miss-rate applications must be kept apart.
An application aggressively using prefetching hardware will also typically have a high LLC miss rate, because prefetch requests for data that is not in the cache are counted as cache misses. To determine if an application is memory-intensive, Power DI uses an experimentally derived threshold of 1, misses per million instructions; an application whose LLC miss rate exceeds that amount is considered memory intensive.
A metric that takes into account both the energy consumption and the performance of the workload is the EDP energy-delay product. Power DI, on the other hand, is able to adjust to the properties of the workload and minimize EDP in all cases, beating both Spread and Cluster—or at least matching them—for every single workload.
A memory domain where all cores are idle is assumed to be in a very low power state and thus consumes 0 units of power. Therefore, for our experiments we constructed eight-application workloads containing from two to six memory-intensive applications.
Before building a scheduler that avoids cache contention, however, we needed to find ways to predict contention.
So by comparing the scheduling assignment constructed based on the actual degradation to that constructed based on the Pain metric, we can evaluate how good the Pain metric is in finding good scheduling assignments. Power DI works as follows: Because these capabilities focus on long-term usage, these capabilities analyze daily data rather than analyze smaller granularity data.Tuning Resource Contention.
Contention occurs when multiple processes try to access the same resource simultaneously. Some processes must then wait for access to various database structures. Topics discussed in this chapter include: Understanding Contention Issues.
Detecting Contention Problems. Solving Contention Problems. Understanding capabilities. 6/05/; 5 minutes to read Contributors. In this article. CPU usage may not have caused meaningful performance degradation or resource contention.
For CPU and networking, then, there should be sustained high usage rather than momentary spikes. Averaging CPU and networking usage throughout the whole. Resource contention reports display the total number of contentions and the total time that was spent waiting for a resource for the modules, functions, source code lines, and instructions in which the waiting occured.
understanding resource contention To build a contention-aware scheduler, we must first understand how to model contention for shared resources. Modeling allows us to predict whether a particular group of threads is likely to compete for shared resources and to.
Understanding Contention Issues. If you detect resource contention with MTS, then first make sure that this is not a memory contention issue by examining the shared pool and the large pool. If performance remains poor, then you may want to create more resources to reduce shared server process contention.
If resource demands exceed. Resource contention profiling collects detailed call stack information each time competing threads in an application are forced to wait for access to a shared resource.
Resource contention reports display the total number of contentions and the total time that was spent waiting for a resource for.Download