metrics

In the CUDA visual profiler, version 5, I know that the "gld/st_requested_throughput" are the requested memory throughput of application. However, when I try to find the actual throughput of hardware, I am confused because there are two pairs of metrics which seem to be qualified, and they are "gld/st_throughput" and "dram_read/write_throughput". Which pair is actually the hardware throughput? And what does the other serve as? Roger Dahl gld/st_throughput includes transactions served by the L1 and L2 caches. While dram_read/write_throughput is the throughput between L2 and device memory. So,

I was looking at some code length metrics other than Lines of Code. Something that Source Monitor reports is statements. This seemed like a valuable thing to know, but the way Source Monitor counted some things seemed unintuitive. For example, a for statement is one statement, even though it contains a variable definition, a condition, and an increment statement. And if a method call is nested in an argument list to another method, the whole thing is considered one statement. Is there a standard way that statements are counted and are their rules governing such a thing? The closest you might

How can I do the equivalent of: @Override public void init(final ServletConfig config) throws ServletException { super.init(config); CsvReporter.enable(new File("/tmp/measurements"), 1, TimeUnit.MINUTES); GraphiteReporter.enable(1, TimeUnit.MINUTES, "my.host.name", 2003); } @Override protected void doGet(final HttpServletRequest req, final HttpServletResponse resp) throws ServletException, IOException { final TimerContext timerContext = Metrics.newMeter(CreateSessionServlet.class,"myservlet-meter", "requests", TimeUnit.SECONDS).time(); try { ... } finally { timerContext.stop(); } with spring

I would like to know if somebody often uses metrics to validate its code/design. As example, I think I will use: number of lines per method (< 20) number of variables per method (< 7) number of paremeters per method (< 8) number of methods per class (< 20) number of field per class (< 20) inheritance tree depth (< 6). Lack of Cohesion in Methods Most of these metrics are very simple. What is your policy about this kind of mesure ? Do you use a tool to check their (e.g. NDepend) ? Imposing numerical limits on those values (as you seem to imply with the numbers) is, in my opinion, not very good

I have followed the instructions here to enable the metrics export to Prometheus for spark. In order to enable metrics export not just from the job, but also from master and workers, I have enabled the jmx agent for all of spark driver, master, worker, and executor. This causes a problem since spark worker and executor are collocated on the same machine and, thus, I need to pass in different jmx ports to them. This is not a problem if I have a 1-1 relationship between spark workers and executors, however, it breaks down in the multiple executors per worker scenario, as there is no way to

Someone told me about swamp diagrams explaning that they were useful to predict code quality by measuring the rate of incoming defects and outgoing fixes on a given product. Unfortunately, I am unable to find additional information on those diagrams and I am wondering if it is a jargon term specific to one company. Can you explain what a swamp diagram is? You can see an example of a "swamp diagram" in this article about the " THE COMMISSIONING AND PERFORMANCE CHARACTERISTICS OF CESR ", page 5 of the pdf or p. 1988 of that document. (CESR is the Cornell Electron Storage Ring, designed to

I am having about 20 APIs and I want to implement statistics like execution time, responses count .. for each API. After doing some research, I came to know that dropwizard metrics is the best approach for implementing such functionalities. I am using Spring MVC framework (non-bootable). Can anybody please suggest me how to integrate Metrics to Spring MVC framework? If possible please provide any code as a reference. You can use Metrics for Spring . Here's a github link , which explains how to integrate it with Spring MVC. The metrics-spring module integrates Dropwizard Metrics library with

The current tf.contrib.metrics.streaming_accuracy is only able to calculate the top 1 accuracy, and not the top k. As a workaround, this is what I've been using: tf.reduce_mean(tf.cast(tf.nn.in_top_k(predictions=predictions, targets=labels, k=5), tf.float32)) However, this does not give me a way to calculate the streaming accuracies averaged across each batch, which would be useful in getting a stable evaluation accuracy. I am currently manually calculating this streaming top 5 accuracy through using its numpy output, but this means I won't be able to visualize this metric on tensorboard. Is