64-bit

Following error occurs when using AdoptOpenJDK and OpenJFX. “Error initializing QuantumRenderer: no suitable pipeline found”

不打扰是莪最后的温柔 提交于 2021-02-07 18:37:41
问题 I am using IntelliJ, Gradle, AdoptOpenJDK 12 and OpenJFX for my project. I am trying creating a simple HelloWorld kind of program to get started with OpenJFX. My project runs fine with OracleJDK but the moment I switch to AdoptOpenJDK 12 and run the project I get the following error in the console: Caused by: java.lang.RuntimeException: Error initializing QuantumRenderer: no suitable pipeline found Caused by: java.lang.RuntimeException: No toolkit found I have tried switching to IBM JDK and

Why the process is getting killed at 4GB?

与世无争的帅哥 提交于 2021-02-07 07:48:11
问题 I have written a program which works on huge set of data. My CPU and OS(Ubuntu) both are 64 bit and I have got 4GB of RAM. Using "top" (%Mem field), I saw that the process's memory consumption went up to around 87% i.e 3.4+ GB and then it got killed. I then checked how much memory a process can access using "uname -m" which comes out to be "unlimited". Now, since both the OS and CPU are 64 bit and also there exists a swap partition, the OS should have used the virtual memory i.e [ >3.4GB +

Is it possible to upgrade a portable Python 32 bit install to a 64 bit install?

拟墨画扇 提交于 2021-02-06 10:14:13
问题 I've been working on a single program for a few months now, which now requires some additional functionality. Originally, a 32 bit install was just fine, but since I'm now working with massive matrices in scipy, I simply do not have the required RAM in 32bit. The other problem I have is that my little project has to be very easily transposeable to new systems belonging to people who have no idea what they're doing and just want to click "run", so I did the whole thing with a portable python

Is it possible to upgrade a portable Python 32 bit install to a 64 bit install?

若如初见. 提交于 2021-02-06 10:12:02
问题 I've been working on a single program for a few months now, which now requires some additional functionality. Originally, a 32 bit install was just fine, but since I'm now working with massive matrices in scipy, I simply do not have the required RAM in 32bit. The other problem I have is that my little project has to be very easily transposeable to new systems belonging to people who have no idea what they're doing and just want to click "run", so I did the whole thing with a portable python

ARM AArch64 stack management

﹥>﹥吖頭↗ 提交于 2021-02-05 08:06:53
问题 Is there no ability to have a 64 bit stack for ARMv8? I understand there is no push and pop instructions on AArch64 so is stack management left to AArch32 for parameter passing and such? How can we pass the 48 bit addresses? I'm overall confused how function calls will work when operating in AArch64. 回答1: Much like in 32-bit * , SP is a valid base register for any load/store instruction, so the mechanics aren't all that different. What is different is that SP is no longer a general-purpose

ARM AArch64 stack management

[亡魂溺海] 提交于 2021-02-05 08:06:25
问题 Is there no ability to have a 64 bit stack for ARMv8? I understand there is no push and pop instructions on AArch64 so is stack management left to AArch32 for parameter passing and such? How can we pass the 48 bit addresses? I'm overall confused how function calls will work when operating in AArch64. 回答1: Much like in 32-bit * , SP is a valid base register for any load/store instruction, so the mechanics aren't all that different. What is different is that SP is no longer a general-purpose

x64 assembly functions (call/return vs push/pop/jump)

瘦欲@ 提交于 2021-02-05 07:57:28
问题 Whats the difference between using the built-in call and return instructions vs manually pushing and popping the stack and using jumps for functions? 回答1: Functionally, if you do it correctly, nothing. However it takes more instructions and/or registers to emulate call / ret using push / pop . Of course if you really wanted to take it to the extreme, you could also emulate push / pop using lea and mov :) Also, current processors have specialized hardware to handle function calls for the

Matplotlib - _tkinter.TclError: bad screen distance “320.0” [closed]

五迷三道 提交于 2021-01-29 07:44:48
问题 This question is unlikely to help any future visitors; it is only relevant to a small geographic area, a specific moment in time, or an extraordinarily narrow situation that is not generally applicable to the worldwide audience of the internet. For help making this question more broadly applicable, visit the help center. Closed 8 years ago . File "main.py", line 52, in <module> r2n(name) File "C:\Users\Riki\Documents\Universita\Erasmus\Personalization and Metadata modeling 02817\Final Project

Matplotlib - _tkinter.TclError: bad screen distance “320.0” [closed]

好久不见. 提交于 2021-01-29 07:31:18
问题 This question is unlikely to help any future visitors; it is only relevant to a small geographic area, a specific moment in time, or an extraordinarily narrow situation that is not generally applicable to the worldwide audience of the internet. For help making this question more broadly applicable, visit the help center. Closed 8 years ago . File "main.py", line 52, in <module> r2n(name) File "C:\Users\Riki\Documents\Universita\Erasmus\Personalization and Metadata modeling 02817\Final Project

Understanding “o^(o-2r)” formula for generating sliding piece moves using unsigned bitboards?

狂风中的少年 提交于 2021-01-28 20:14:32
问题 What I Am Trying To Do I am trying to perform some bitwise operations to create a chess engine. To make this engine, I need to be able to generate moves for pieces, like rooks. There is a handy formula for creating a bitboard of squares available for the rook to move to: bitboardOfOccupiedSquares ^ (bitboardOfOccupiedSquares - 2 * bitboardOfPieceToMove) . Consider the following chess board position: I am trying to generate all of the squares that the rook on h1 can move to. So this should be