TZP | TorZillaPrint : Firefox & Tor Browser fingerprint | Authentication library
kandi X-RAY | TZP Summary
kandi X-RAY | TZP Summary
TorZillaPrint: Firefox & Tor Browser fingerprint testing
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of TZP
TZP Key Features
TZP Examples and Code Snippets
Community Discussions
Trending Discussions on TZP
QUESTION
I'm trying to find out where/how to implement subquery in below trimmed down example.
The issue is that I need to add hours depending on time zones and then return those fields.
At the same time though, I need to filter by the same fields, and for it to be accurate, I need them to already be adjusted to the correct time zone.
Can you please give me an advice on how I can work this out?
...ANSWER
Answered 2019-Jul-31 at 11:59You must repeat the CASE statement in the WHERE clause:
QUESTION
EIDT
Change the code as Robert suggested, but thrust is still much slower.
The data I used is based on two .dat files, so I omit it in the code.
Original problem
I have two complex vectors which have been put on GPU Tesla M6. I want to compute element-wise product of the two vectors, namely [x1*y1,...,xN*yN]. The length of two vectors are both N = 720,896.
Code snippet(modified)
I solve this problem in two ways. One is using thrust with type conversion and a specific struct:
...ANSWER
Answered 2019-May-18 at 20:49CUDA kernel launches are asynchronous. This means that control is returned to the host thread so that it can proceed with the next line of code after the kernel launch before the kernel has even started to execute.
This is covered in numerous questions here on the cuda
tag. This is a common mistake when timing CUDA code. It can affect the way you time thrust code as well as the way you time ordinary CUDA code. The usual solution is to insert a cudaDeviceSynchronize() call before closing the timing region. This ensures that all CUDA activity is complete when you finish your timing measurement.
When I turned what you have into a complete code with proper timing methods, the thrust code was actually faster. Your kernel design is inefficient. Here is my version of your code, running on CUDA 10 on a Tesla P100, showing that the timing between the two cases is nearly the same:
QUESTION
I have copied program on Simulated Annealing from a book (first result link here) and am facing the below issues on compilation, for below line in main().
...ANSWER
Answered 2019-Mar-23 at 12:08pop()
is a function and is being indexed as if it were a variable. With a quick look there is an array op
which might be what’s needed here. So maybe it should be op[x]
and not pop[x]
in these places?
And when looking at the original that’s how it is. So a copying error by user, should be closed.
QUESTION
I'm trying to make a simple benchmarking algorithm, to compare different operations. Before I moved on to the actual functions i wanted to check a trivial case with a well-documented outcome : multiplication vs. division.
Division should lose by a fair margin from the literature i have read. When I compiled and ran the algorithm the times were just about 0. I added an accumulator that is printed to make sure the operations are actually carried out and tried again. Then i changed the loop, the numbers, shuffled and more. All in order to prevent any and all things that could cause "divide" to do anything but floating point division. To no avail. The times are still basically equal.
At this point I don't see where it could weasel its way out of the floating point divide and I give up. It wins. But I am really curious why the times are so close, what caveats/bugs i missed, and how to fix them.
(I know filling the vector with random data and then shuffling is redundant but I wanted to make sure the data was accessed and not just initialized before the loop.)
("String compares are evil", i am aware. If it is the cause of the equal times, i will gladly join the witch hunt. If not, please don't mention it.)
compile:
...ANSWER
Answered 2018-Apr-20 at 10:58You aren't just measuring the speed of multiplication/divide. If you put your code into https://godbolt.org/ you can see the assembly generated.
You are measuring the speed of calling a function and then doing multiply/divide inside the function. The time taken for the single multiply/divide instruction is tiny compared to the cost of the function calls so gets lost in the noise. If you move your loop to inside your function you'll probably see more of a difference. Note that with the loop inside your function your compiler may decide to vectorise your code which will still show whether there is a difference between multiply and divide but it wont be measuring the difference for the single mul/div instruction.
QUESTION
I wrote a simple program to compare Rust and C performance.
The Rust version:
...ANSWER
Answered 2017-Jun-07 at 12:33Rust compiles the loop to:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install TZP
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page