profiling

问题 I am currently writing a time consuming python program and decided to rewrite part of the program in fortran. However, the performance is still not good. For profiling purpose, I want to know how much time is spent in f2py wrappers and how much time is actual spent in fortran subroutines. Is there a convenient way to achieve this? 回答1: At last I found out -DF2PY_REPORT_ATEXIT option can report wrapper performance. 来源： https://stackoverflow.com/questions/35968682/how-to-obtain-how-much-time-is

问题 I recently hit a severe performance wall in an AnyLogic model and decided to do some method profiling. The top-level culprit was com.anylogic.engine.Engine.mc() , but what does it do, and how do we speed it up? 回答1: Ideally, never use conditional transitions, only message-based, timeout-based and agent-arrival-based ones. Otherwise, your condition-based transition keeps checking all the time if it's condition has been met yet. 回答2: It was explained to me that com.anylogic.engine.Engine.mc()

问题 I am trying to familiarize myself with perf and run it against various programs I wrote. When I launch it against program that is 100% single threaded, perf shows that it takes two cores on machine (task-clock event). Here's the example output: perf stat -a --per-core python3 test.py Performance counter stats for 'system wide': S0-C0 1 19004.951263 task-clock (msec) # 1.000 CPUs utilized (100.00%) S0-C0 1 5,582 context-switches (100.00%) S0-C0 1 19 cpu-migrations (100.00%) S0-C0 1 3,746 page

问题 I have created a very simple form submission for users to register, requiring them to enter their email, username, and password. There is a ~5 sec delay from when I click the submit button to when the form actually submits. How can I figure out what's going on here? Here is what I have tried so far: Django Debug Toolbar - Profiling: It seems that there are clues here, but I have been unable to use this information to solve the issue using this data. Any ideas? Profiling Image Javascript I

问题 I would like to use system.time in R to get the total CPU time on a multicore function. The problem is that system.time does obviously not capture CPU time spend by the child processes spawned by the parallel package. library(doParallel) cl <- makeCluster(2) registerDoParalllel(2) timings <- system.time(foreach(i = 1:2) %do% rnorm(1e8)) Timings then looks like this > timings user system elapsed 16.883 5.731 22.899 The timings add up. Now if I use parallel processing: timings <- system.time

问题 I wrote a few kernel function and wonder how many miliseconds to process these functions. using namespace std; #include <iostream> #include <stdio.h> #include <stdlib.h> #define N 8000 void fillArray(int *data, int count) { for (int i = 0; i < count; i++) data[i] = rand() % 100; } __global__ void add(int* a, int *b) { int add = 0; int tid = threadIdx.x + blockIdx.x * blockDim.x; if (tid < N) { add = a[tid] + b[tid]; } } __global__ void subtract(int* a, int *b) { int subtract = 0; int tid =

问题 My kernel archive 100% utilization, but the kernel time is at only 3% and there is no time overlap between memory copies and kernels . Especially the high utilization and the low kernel time don't make sense to me. So how should I proceed in optimizing my kernel? I already made sure, that I only have coalesced and pinned memory access, like the profiler recommended. `Quadro FX 580 utilization = 100.00% (62117.00/62117.00)` Kernel time = 3.05 % of total GPU time Memory copy time = 0.9 % of

问题 I'm using nvprof to get the number of global memory accesses for the following CUDA code. The number of loads in the kernel is 36 (accessing d_In array) and the number of stores in the kernel is 36+36 (for accessing d_Out array and d_rows array). So, the total number of global memory loads is 36 and the number of global memory stores is 72. However, when I profile the code with nvprof CUDA profiler, it reports the following: (Basically I want to compute the Compute to Global Memory Access

问题 I'm trying to profile a function in another method, so in order to measure its time, I'm doing something like this: double diffTime = GetCurrentTime() - m_lastTime; SuspendOtherProcessThreads(); double runningTime += diffTime; ... Do profiling stuff ... ResumeOtherProcessThreads(); m_lastTime = GetCurrentTime(); ... Let profiled process run .... This is what I do each sample and I consider the time in which I sampled to be "runningTime". But for some reason I get that "runningTime" is much

问题 I am profiling a python code ; why does it spend more time in the user space ? user@terminal$ time python main.py 1964 function calls in 0.003 CPU seconds Ordered by: standard name ncalls tottime percall cumtime percall filename:lineno(function) 1 0.000 0.000 0.003 0.003 :1() 1 0.000 0.000 0.000 0.000 ConfigParser.py:218( init ) 1 0.000 0.000 0.001 0.001 ConfigParser.py:266(read) 30 0.000 0.000 0.000 0.000 ConfigParser.py:354(optionxform) 1 0.000 0.000 0.000 0.000 ConfigParser.py:434(_read)