parallel-processing

问题 Let me start with thank you for taking the time for reading this. First off I would like to start to share my code, see blow. It is around 200 lines of code but most of the lines are property definitions. Some context: I am just trying to get a beter understanding of multiprocessing so I went and made a small project to try and run multiple processes which all share a single input and output queue. It works, but sometimes it deadlocks right after calling the scheduler.start() method. I am

问题 I am required to process N fixed-length strings in parallel on an OpenCL device. Processing a string involves calling function that is provided, that takes a string as input represented as a buffer, and the length of the string in that buffer. void Function(const char *input_buffer, const int string_length, const char *output_buffer) Inside the host application I have concatenated the N strings into a large char buffer, with no separator between them. I would like to create a kernel with a

问题 I have an issue with cleaning up network-synced objects: On each tick the server sends the client (Unreliably) the states (Position, Quaternion) of objects around that player. In order to increase performance on the Client side, I wish to clean (hide or dispose) of objects that are not updated for X time. I came up with the following idea: Implement a mechanism similar to JavaScript's setTimeout . The mechanism should support mid-run termination as well as restarting with the same set of args

问题 I am currently using the function read.fasta() from the R package seqinr. I think that creating an index file already make the reading faster but I was wondering if there was already another function to load it faster ? I looked for the function read.big.fasta() from PopGenome, but the package has been removed from CRAN and Bioconductor, so I am not so sure about it anymore. Any advices? 回答1: You can use readDNAStringSet from Biostrings . Get the human genome: download.file("https:/

问题 There are three source files. Each source files are corresponding targets. For example, the target of a.cpp is a, b.cpp is b, and c.cpp is c. src/a.cpp b.cpp c.cpp build/ I can build the targets parallelly using -j option. For example, cd build make -j3 target a, b, and c build parallelly. Is there any way to specify some of targets and parallel build ? For example make -j2 a b Unfortunately, it works sequentially. First, build a and then build b . I want to build only a and b parallelly. I

问题 This question already has answers here : How to tell a lambda function to capture a copy instead of a reference in C#? (4 answers) Closed 1 year ago . I have created a console app which print the number in a for loop using C# Task.Run() method. If you look at the code, I am initializing the Employee object in the loop and pass the number in the method, though it prints the same number. Code: class Program { static void Main(string[] args) { for (int i = 1; i <= 500; i++) { Task.Run(() => new

问题 I would like to implement in Apache Flink the following scenario: Given a Kafka topic having 4 partitions, I would like to process the intra-partition data independently in Flink using different logics, depending on the event's type. In particular, suppose the input Kafka topic contains the events depicted in the previous images. Each event have a different structure: partition 1 has the field " a " as key, partition 2 has the field " b " as key, etc. In Flink I would like to apply different

问题 The below code works fine, however, I am interested to run it in parallel. I have tried different plans within future and future.apply but couldn't managed. Any help appreciated. I am running on windows OS, 8 cores. library(mlr3verse) library(future.apply) #> Warning: package 'future.apply' was built under R version 3.6.3 #> Loading required package: future #> Warning: package 'future' was built under R version 3.6.3 library(future) future::plan(multicore) tsk_clf = tsk("sonar") tsk_clf$col

问题 I have a slow function that I want to apply to each row in a data.frame. The computation is embarrassingly parallel. I have 4 cores, but R's built in functions only uses one. All I want to do is a parallel equivalent to: data$c = slow.foo(data$a, data$b) I can't find clear instructions on which library to use (overwhelmed by choice) and how to use it. Any help would be greatly appreciated. 回答1: The parallel package is included with base R. Here's a quick example using parApply from that

问题 My issue is that if I reassign an item in a list such that the reassignment happens during a parallel process, then after the parallel processes are finished, the change reverts back to its original state. In the below example- greatly simplified for ease of understanding-, I have a function that changes the list element NoZeros[0] to "chicken" and a second function that changes NoZeros[1] to "sandwich". I even put "global" in the second function just to demonstrate that this isn't a local vs