benchmarking

问题 Are there any tools available the can help benchmark some code in Kotlin? I can use something similar to the approaches suggested here: http://www.ibm.com/developerworks/java/library/j-benchmark1/index.html - but I was wondering if there were any Kotlin native tools, so that i wouldn't necessarily have to reinvent the wheel! 回答1: For benchmarking use JMH. This framework can help you write most relevant benchmark, and know a lot about how JVM works. There are old project on github, but i hope

问题 In another thread I started a discussion about Vectors and Arrays, in which I was largely playing devil's advocate, to push buttons. However, during the course of this, I stumbled onto a test case that has me a little perplexed, and I would like to have a real discussion about it, over the "abuse" I'm getting for playing devil's advocate, starting a real discussion on that thread is now impossible. However, the particular example has me intrigued, and I cannot explain it to myself

问题 In another thread I started a discussion about Vectors and Arrays, in which I was largely playing devil's advocate, to push buttons. However, during the course of this, I stumbled onto a test case that has me a little perplexed, and I would like to have a real discussion about it, over the "abuse" I'm getting for playing devil's advocate, starting a real discussion on that thread is now impossible. However, the particular example has me intrigued, and I cannot explain it to myself

问题 I am trying to benchmark a few method of itertools against generators and list comprehensions. The idea is that I want to build an iterator by filtering some entries from a base list. Here is the code I came up with(Edited after accepted answer): from itertools import ifilter import collections import random import os from timeit import Timer os.system('cls') # define large arrays listArrays = [xrange(100), xrange(1000), xrange(10000), xrange(100000)] #Number of element to be filtered out nb

问题 I am trying to benchmark a few method of itertools against generators and list comprehensions. The idea is that I want to build an iterator by filtering some entries from a base list. Here is the code I came up with(Edited after accepted answer): from itertools import ifilter import collections import random import os from timeit import Timer os.system('cls') # define large arrays listArrays = [xrange(100), xrange(1000), xrange(10000), xrange(100000)] #Number of element to be filtered out nb

问题 I'm about to start doing some benchmarking/testing of our builds, and I'd like to drive the whole thing from a command line. I am aware of DevEnv but am not convinced it can do what I want. If I could have a single file built within a single project, I'd be happy. Can this be done? 回答1: The magical incantation is as follows. Note that this has only been tested with VS 2010 - I have heard this is the first version of Visual Studio with this capability: The Incantation <msbuild> <project>

问题 I wanted to increase the performance of my backend REST API on a certain operation that polled multiple different external APIs sequentially and collected their responses and flattened them all into a single list of responses. Having just recently learned about CompletableFuture s, I decided to give it a go, and compare that solution with the one that involved simply changing my stream for a parallelStream . Here is the code used for the benchmark-test: package com.alithya.platon; import java

问题 Python takes 17 times longer to load on my Windows 7 machine than Ubuntu 14.04 running on a VM (inside Windows on the same hardware). Anaconda3 distribution is used on Windows and Ubuntu default python3.4. From a Bash prompt (Git bash on Windows): $ time python3 -c "pass" returns in 0.614s on Windows and 0.036s on Linux When packages are loaded the situation gets worse: $ time python3 -c "import matplotlib" returns in 6.01s on Windows and 0.189s on Linux Spyder takes a whopping 51s to load on

问题 Are there any links/resources towards performance benchmarks for Lucene/Solr on large datasets. Data sets above the range of 500GB ~ 5TB Thanks 回答1: Lucene committer Mike McCandless runs benchmarks on a regular basis to track down performances improvements and regressions. They are made with Wikipedia exports, which might be a little bit smaller than what you are looking for. But the performance doesn't depend so much on the input size, but rather on the number of documents and unique terms.

问题 In trying to write an optimized DSP algorithm, I was wondering about relative speed between stack allocation and heap allocation, and size limits of stack-allocated arrays. I realize there is a stack frame size limit, but I don't understand why the following runs, generating seemingly realistic benchmark results with cargo bench , but fails with a stack overflow when run with cargo test --release . #![feature(test)] extern crate test; #[cfg(test)] mod tests { use test::Bencher; #[bench] fn it