parallel-processing

问题 How 'capacity' is calculated? From their documentation The "capacity" metric is very useful and tells you what % of the time in the last 10 minutes the bolt spent executing tuples. If this value is close to 1, then the bolt is "at capacity" and is a bottleneck in your topology. The solution to at-capacity bolts is to increase the parallelism of that bolt. I don't quite understand % of time. So if the value is 0.75 - what does it really mean? 回答1: It's the percent of time that the bolt is busy

问题 I have a lot of files I need to download. I am using download.file() function and furrr::map to download in parallel, with plan(strategy = "multicore") . Please advise how can I load more jobs for each future? Running on Ubuntu 18.04 with 8 cores. R version 3.5.3. The files can be txt, zip or any other format. Size varies in range of 5MB - 40MB each. 回答1: Using furrr works just fine. I think what you mean is furrr::future_map . Using multicore substantially increases the downloading speed (

问题 I'm trying to implement a parallel implementation for both Iterable and Sequence in Kotlin. I got a little file, it consists of 4 extension functions, but the third one gives me an compiler error: suspend fun <T, R> Iterable<T>.parallelMap(block: suspend(T) -> R) = coroutineScope { map { async { block(it) } }.map { it.await() } } suspend fun <T> Iterable<T>.parallelForEach(block: suspend (T) -> Unit) = coroutineScope { map { async { block(it) } }.forEach { it.await() } } suspend fun <T, R>

问题 I'm pretty new to Python and completely new to parallel processing. I've been writing code to analyze punctate image data (think PALM lite) and trying to speed up my analysis code using the multiprocessing module. For small data sets I see a pretty decent speed-up up to four cores. For large datasets I start getting an AssertionError. I tried to make a boiled down example which produces the same error, see below: import numpy as np import multiprocessing as mp import os class TestClass(object

问题 I'm pretty new to Python and completely new to parallel processing. I've been writing code to analyze punctate image data (think PALM lite) and trying to speed up my analysis code using the multiprocessing module. For small data sets I see a pretty decent speed-up up to four cores. For large datasets I start getting an AssertionError. I tried to make a boiled down example which produces the same error, see below: import numpy as np import multiprocessing as mp import os class TestClass(object

问题 I just bumped into some strange behaviour of DBMS_PARALLEL_EXECUTE (at least for me). See my preset (executed as SYS): -- Preset drop user usr1 cascade; create user usr1 identified by usr1; create or replace procedure usr1.do_stuff(p1 in number, p2 in number) is begin dbms_output.put_line('I did stuff!'); end; / drop user usr2 cascade; create user usr2 identified by usr2; grant connect to usr2; grant create job to usr2; drop role stuff_doer cascade; create role stuff_doer; grant execute on

问题 I just bumped into some strange behaviour of DBMS_PARALLEL_EXECUTE (at least for me). See my preset (executed as SYS): -- Preset drop user usr1 cascade; create user usr1 identified by usr1; create or replace procedure usr1.do_stuff(p1 in number, p2 in number) is begin dbms_output.put_line('I did stuff!'); end; / drop user usr2 cascade; create user usr2 identified by usr2; grant connect to usr2; grant create job to usr2; drop role stuff_doer cascade; create role stuff_doer; grant execute on

问题 The idea im trying to run is this: RUN 3 Processes doing calculation ONCE one of the 3 processes finishes the task KILL others imediatly and continue with the main task, i can't let it run any second longer The things i've tried was: Putting the global variable through multiprocessing.manager, but that still lets processes finish their loops. Raising an exception OS: Windows PYTHON: 2.7 def f(name): Doing = True try: while Doing: print 'DOING',name somecodethatmarksDoingAsFalse() except

问题 TL;DR: Is there any way to get SGE to round-robin between servers when scheduling jobs, instead of allocating all jobs to the same server whenever it can? Details: I have a large compute process that consists of many smaller jobs. I'm using SGE to distribute the work across multiple servers in a cluster. The process requires a varying number of tasks at different points in time (technically, it is a DAG of jobs). Sometimes the number of parallel jobs is very large (~1 per CPU in the cluster),

问题 I have an algorithm and I've been trying to accelerate it using OpenCL on my nVidia. It has to process a large amount of data (let's say 100k to milions), where for each one datum: a matrix (on the device) has to be updated first (using the datum and two vectors); and only after the whole matrix has been updated, the two vectors (also on the device) are updated using the same datum. So, my host code looks something like this for (int i = 0; i < milions; i++) { clSetKernelArg(kernel