parallel-processing

问题 I saw a "similar" post Executing MySQL SELECT * query in parallel, buy my question is different, and this has not been answered either, so i guess its not a duplicate. I am trying to do a MySQL select request in parallel. The reason is because i need the response fast. I managed to create the request when i paralleled the connection as well, but as the connection takes more time then the actual select it would be faster to connect one time, and do the select in parallel. My approach: import

问题 I am a bit confused how it is possible that Warps diverge and need to be synchronized via __syncthreads() function. All elements in a Block handle the same code in a SIMT fashion. How could it be that they are not in sync? Is it related to the scheduler? Do the different warps get different computing times? And why is there an overhead when using __syncthreads() ? Lets say we have 12 different Warps in a block 3 of them have finished their work. So now there are idling and the other warps get

问题 Whats an appropriate way to profile parallel code in julia? When I run @profile foo(...) where foo is my function, I get julia> Profile.print() 1234 task.jl; anonymous; line: 23 4 multi.jl; remotecall_fetch; line: 695 2 multi.jl; send_msg_; line: 172 2 serialize.jl; serialize; line: 74 2 serialize.jl; serialize; line: 299 2 serialize.jl; serialize; line: 130 2 serialize.jl; serialize; line: 299 1 dict.jl; serialize; line: 369 1 serialize.jl; serialize_type; line: 278 1 serialize.jl; serialize

问题 Whats an appropriate way to profile parallel code in julia? When I run @profile foo(...) where foo is my function, I get julia> Profile.print() 1234 task.jl; anonymous; line: 23 4 multi.jl; remotecall_fetch; line: 695 2 multi.jl; send_msg_; line: 172 2 serialize.jl; serialize; line: 74 2 serialize.jl; serialize; line: 299 2 serialize.jl; serialize; line: 130 2 serialize.jl; serialize; line: 299 1 dict.jl; serialize; line: 369 1 serialize.jl; serialize_type; line: 278 1 serialize.jl; serialize

问题 Once I had the theory that on modern operating systems multithreaded read access on the HDD should perform better. I thought that: the operating system queues all read requests, and rearranges them in such a way, that it could read from the HDD more sequentially. The more requests it would get, the better it could rearrange them to optimize the read sequence. I was very sure that I read it somewhere few times. But I did some benchmarking, and had to find out, that multithreaded read access

问题 Once I had the theory that on modern operating systems multithreaded read access on the HDD should perform better. I thought that: the operating system queues all read requests, and rearranges them in such a way, that it could read from the HDD more sequentially. The more requests it would get, the better it could rearrange them to optimize the read sequence. I was very sure that I read it somewhere few times. But I did some benchmarking, and had to find out, that multithreaded read access

问题 I have scenarios where I will need to process thousands of records at a time. Sometime, it might be in hundreds, may be upto 30000 records. I was thinking of using the scala's parallel collection. So just to understand the difference, I wrote a simple pgm like below: object Test extends App{ val list = (1 to 100000).toList Util.seqMap(list) Util.parMap(list) } object Util{ def seqMap(list:List[Int]) = { val start = System.currentTimeMillis list.map(x => x + 1).toList.sum val end = System

问题 In Brief I have a Windows Service that executes several jobs as async Tasks in parallel. However, when the OnStop is called, it seems that these are all immediately terminated instead of being allowed to stop in a more gracious manner. In more detail Each job represents an iteration of work, so having completed its work the job then needs to run again. To accomplish this, I am writing a proof-of-concept Windows Service that: runs each job as an awaited async TPL Task (these are all I/O bound

问题 I am doing parallel steps as - stages { stage ('Parallel build LEVEL 1 - A,B,C ...') { steps{ parallel ( "Build A": { node('Build_Server_Stack') { buildAndArchive(A) // my code } }, "Build B" : { node('Build_Server_Stack') { buildAndArchive(B) } }, "Build C" : { node('Build_Server_Stack') { buildAndArchive(C) } } ) } } } Now I require to start the execution of B, after C is done. I can pull the B job out of the parallel block and add after the parallel block to achieve this. But in that case

问题 I am doing parallel steps as - stages { stage ('Parallel build LEVEL 1 - A,B,C ...') { steps{ parallel ( "Build A": { node('Build_Server_Stack') { buildAndArchive(A) // my code } }, "Build B" : { node('Build_Server_Stack') { buildAndArchive(B) } }, "Build C" : { node('Build_Server_Stack') { buildAndArchive(C) } } ) } } } Now I require to start the execution of B, after C is done. I can pull the B job out of the parallel block and add after the parallel block to achieve this. But in that case