benchmarking

I read in a comment here on Stack Overflow that it is more memory efficient to do slice assignment when changing lists. For example, a[:] = [i + 6 for i in a] should be more memory efficient than a = [i + 6 for i in a] because the former replaces elements in the existing list, while the latter creates a new list and rebinds a to that new list, leaving the old a in memory until it can be garbage collected. Benchmarking the two for speed, the latter is slightly quicker: $ python -mtimeit -s 'a = [1, 2, 3]' 'a[:] = [i + 6 for i in a]' 1000000 loops, best of 3: 1.53 usec per loop $ python -mtimeit

What is considered the most accurate way to benchmark VBA code (in my case, I am testing code in Excel)? Are there any other techniques for benchmarking code besides the 2 below, and if so, what are the pros/cons of the method? Here are 2 popular methods. First: Timer Sub TimerBenchmark() Dim benchmark As Double benchmark = Timer 'Do your code here MsgBox Timer - benchmark End Sub And Tick (which I see argued as the most accurate): Option Explicit Private Declare Function GetTickCount Lib "kernel32" () As Long Sub TickBenchmark() Dim Start As Long Dim Finish As Long Start = GetTickCount() 'Do

Why is :memory: in sqlite so slow? I've been trying to see if there are any performance improvements gained by using in-memory sqlite vs. disk based sqlite. Basically I'd like to trade startup time and memory to get extremely rapid queries which do not hit disk during the course of the application. However, the following benchmark gives me only a factor of 1.5X in improved speed. Here, I'm generating 1M rows of random data and loading it into both a disk and memory based version of the same table. I then run random queries on both dbs, returning sets of size approx 300k. I expected the memory

I would like to write a program that makes extensive use of BLAS and LAPACK linear algebra functionalities. Since performance is an issue I did some benchmarking and would like know, if the approach I took is legitimate. I have, so to speak, three contestants and want to test their performance with a simple matrix-matrix multiplication. The contestants are: Numpy, making use only of the functionality of dot . Python, calling the BLAS functionalities through a shared object. C++, calling the BLAS functionalities through a shared object. Scenario I implemented a matrix-matrix multiplication for

Does anyone ever use stopwatch benchmarking, or should a performance tool always be used? Are there any good free tools available for Java? What tools do you use? To clarify my concerns, stopwatch benchmarking is subject to error due to operating system scheduling. On a given run of your program the OS might schedule another process (or several) in the middle of the function you're timing. In Java, things are even a little bit worse if you're trying to time a threaded application, as the JVM scheduler throws even a little bit more randomness into the mix. How do you address operating system