out-of-memory

问题 After updating Embarcadero C++ Builder to a new version, our project suddenly fails to build. This happens just with one of our projects. For the most of the team members, identical code builds without errors. On my computer, linking fails every time. In Build tab: [ilink32] Fatal: Out of memory In Output tab: Build FAILED. c:\program files (x86)\embarcadero\studio\18.0\Bin\CodeGear.Cpp.Targets(3517,5): error : Fatal: Out of memory There is no more information. If I enable Link with Dynamic

问题 I have started facing Native memory allocation issue. I guess could be related with the -Xmx and -Xms settings. What is the recommended way to set this values ? Currently I have: -Xmx13G -Xms6G I read that is recommended to set same values but without any explanation of why. The error I am getting is : # There is insufficient memory for the Java Runtime Environment to continue. # Native memory allocation (mmap) failed to map 746061824 bytes for committing reserved memory. # Possible reasons:

问题 This question already has an answer here : Why does a large local array crash my program, but a global one doesn't? [duplicate] (1 answer) Closed 2 years ago . When I write the following program, it works correctly i.e. the bitset array is declared outside the main() method. Correctly Works #include <iostream> #include <bitset> using namespace std; bitset<5000> set[5000]; int main(){ cout<<"program runs fine"<<endl; return 0; } But I get stack-overflow exception when I create it inside the

问题 This question already has an answer here : Why does a large local array crash my program, but a global one doesn't? [duplicate] (1 answer) Closed 2 years ago . When I write the following program, it works correctly i.e. the bitset array is declared outside the main() method. Correctly Works #include <iostream> #include <bitset> using namespace std; bitset<5000> set[5000]; int main(){ cout<<"program runs fine"<<endl; return 0; } But I get stack-overflow exception when I create it inside the

问题 This question already has an answer here : Why does a large local array crash my program, but a global one doesn't? [duplicate] (1 answer) Closed 2 years ago . When I write the following program, it works correctly i.e. the bitset array is declared outside the main() method. Correctly Works #include <iostream> #include <bitset> using namespace std; bitset<5000> set[5000]; int main(){ cout<<"program runs fine"<<endl; return 0; } But I get stack-overflow exception when I create it inside the

问题 This question already has an answer here : Why does a large local array crash my program, but a global one doesn't? [duplicate] (1 answer) Closed 2 years ago . When I write the following program, it works correctly i.e. the bitset array is declared outside the main() method. Correctly Works #include <iostream> #include <bitset> using namespace std; bitset<5000> set[5000]; int main(){ cout<<"program runs fine"<<endl; return 0; } But I get stack-overflow exception when I create it inside the

问题 I found several questions on Stackoverflow regarding this topic (some of them without any answer) but nothing related (so far) with this error in regression. I'm, running a probit model in r with (I'm guessing) too many fixed effects (year and places): myprobit <- glm(factor(Y) ~ factor(T) + factor(X1) + factor(X2) + factor(X3) + factor(YEAR) + factor(PLACE), family = binomial(link = "probit"), data = DT) The PLACE variable has about 1000 unique values and YEAR 8 values. The dataset DT has 13

问题 I am making a Stochastic Gradient Descent Classifier (SGDClassifier) using scikit- learn. While Fitting my training data (of shape (60000,784)), I am getting memory error. How to fix it? I have already tried switching from 32 bit to 64 bit IDE. And reducing the training data will decrease the performance (that is basically not the option). Code: (Python 3.7) # Classification Problem # Date: 1st September 2019 # Author: Pranay Saha import pandas as pd x_train= pd.read_csv('mnist_train.csv') y

问题 From the Netty api, it shows the request will be queued if isWritable return false. Could I know where will the request be queued? In what case, the queue could be full and cause OOM issue? Following is the document for isWritable() Returns true if and only if the I/O thread will perform the requested write operation immediately. Any write requests made when this method returns false are queued until the I/O thread is ready to process the queued write requests. https://netty.io/4.1/api/io

问题 If i have following code to send data repeatedly to client every 10ms: setInterval(function() { res.write(somedata); }, 10ms); What would happen if the client is very slow to receive the data? Will server get out-of-memory error? Edit: actually the connection is kept alive, sever send jpeg data endlessly (HTTP multipart/x-mixed-replace header + body + header + body.....) Because node.js response.write is asynchronous, so some users guess it may store data in internal buffer and wait until low