concurrency

问题 I've been doing some experimentation lately on using large numbers of random numbers to generate "normal distribution" bell curves. The approach is simple: Create an array of integers and zero it out. (I'm using 2001 integers) Repeatedly calculate indexes in this array and index that entry in the array, as follows Loop either 999 or 1000 times. On each iteration: Seed an array index with the center value (1000) Generate a random number = +1/-1. and add it to the array index at the end of the

问题 I have some scheduled jobs in my SQL Agent: Job1, executing every 2 minutes Job2, executing every 10 minutes Job3, executing every 15 minutes As you can see, multiple jobs can run simultaneously. When these jobs do run simultaneously, it is causing the CPU usage to go to 100%. Is there a solution? Is there a way to control the number of jobs running concurrently? Note: I need these jobs to run approximately in their appropriate period. 回答1: Use a session lock via sp_getapplock You're asking

问题 I had a problem with catching my exception from Task.Run I changed my code and my problem solved. I'm willing to figure out what is the difference between handling exceptions inside Task.Run in these two ways : In Outside function I can't catch the exception but in Inside I can catch it. void Outside() { try { Task.Run(() => { int z = 0; int x = 1 / z; }); } catch (Exception exception) { MessageBox.Show("Outside : " + exception.Message); } } void Inside() { Task.Run(() => { try { int z = 0;

问题 I had a problem with catching my exception from Task.Run I changed my code and my problem solved. I'm willing to figure out what is the difference between handling exceptions inside Task.Run in these two ways : In Outside function I can't catch the exception but in Inside I can catch it. void Outside() { try { Task.Run(() => { int z = 0; int x = 1 / z; }); } catch (Exception exception) { MessageBox.Show("Outside : " + exception.Message); } } void Inside() { Task.Run(() => { try { int z = 0;

问题 Scenario: User taps a button asking for some kind of modification on address book. A method is called to start this modification and an alert view is shown. In order to show the alert view and keep the UI responsive, I used dispatch_queue: dispatch_async(dispatch_get_global_queue(DISPATCH_QUEUE_PRIORITY_DEFAULT, 0), ^{ dispatch_sync(dispatch_get_main_queue(), ^{ // Show the alert view }); }); Start the process of address book modification using: dispatch_async(modifyingAddressBookQueue, ^{});

问题 Scenario: User taps a button asking for some kind of modification on address book. A method is called to start this modification and an alert view is shown. In order to show the alert view and keep the UI responsive, I used dispatch_queue: dispatch_async(dispatch_get_global_queue(DISPATCH_QUEUE_PRIORITY_DEFAULT, 0), ^{ dispatch_sync(dispatch_get_main_queue(), ^{ // Show the alert view }); }); Start the process of address book modification using: dispatch_async(modifyingAddressBookQueue, ^{});

问题 I'm learning about deadlocks in Java, and there's this sample code from Sun's official tutorial: Alphonse and Gaston are friends, and great believers in courtesy. A strict rule of courtesy is that when you bow to a friend, you must remain bowed until your friend has a chance to return the bow. Unfortunately, this rule does not account for the possibility that two friends might bow to each other at the same time. public class Deadlock { static class Friend { private final String name; public

问题 I'm learning about deadlocks in Java, and there's this sample code from Sun's official tutorial: Alphonse and Gaston are friends, and great believers in courtesy. A strict rule of courtesy is that when you bow to a friend, you must remain bowed until your friend has a chance to return the bow. Unfortunately, this rule does not account for the possibility that two friends might bow to each other at the same time. public class Deadlock { static class Friend { private final String name; public

问题 I am trying to properly understand and implement two concurrently running Task objects using Python 3's relatively new asyncio module. In a nutshell, asyncio seems designed to handle asynchronous processes and concurrent Task execution over an event loop. It promotes the use of await (applied in async functions) as a callback-free way to wait for and use a result, without blocking the event loop. (Futures and callbacks are still a viable alternative.) It also provides the asyncio.Task() class

问题 I am trying to properly understand and implement two concurrently running Task objects using Python 3's relatively new asyncio module. In a nutshell, asyncio seems designed to handle asynchronous processes and concurrent Task execution over an event loop. It promotes the use of await (applied in async functions) as a callback-free way to wait for and use a result, without blocking the event loop. (Futures and callbacks are still a viable alternative.) It also provides the asyncio.Task() class