Parallel processing Just how good is VTK?

I have heard some good reviews of the Visualization ToolKit (VTK) from developers. But exactly how powerful is it? For example, can it handle visualization of an entire oil reservoir (in a simulator) with billions of grids points? Most industrial reservoir simulators run on parallel processors. I know VTK supports parallel processing, but again how stable can it be utilizing parallel processors when running something like reservoir simulations? Has anybody used VTK on such a large scale project?

Parallel processing Non-trivial private data in Fortran90 OpenMP

I have a section of a Fortran90 program that should be parallelized with OpenMP. !$omp parallel num_threads(8) & !$omp private(j, s, prop_states) & !$omp firstprivate(targets, pulses) ! ... modify something in pulses. targets(s)%ham contains pointers to ! elements of pulses ... do s = 1, n_systems prop_states(s) = targets(s)%psi_i call prop(prop_states(s), targets(s)%grid, targets(s)%ham, & & targets(s)%work, para) end do !$omp end parallel What I'

Parallel processing What do i need to run multiple computers as one?

How can i run multiple computers as one? i.e. one "master" which issues commands and one or more slaves who do what they are told to do. also, How do the distributed computingsystems in supercomputers do this? EDIT: I found this, this and this and now i wonder, is there something similar that will run parallel programs like hash cracking? Mostly a software designed for these types of cloud computing systems.

Parallel processing Whether go uses shared memory or distributed computing

Go has the slogan "Do not communicate by sharing memory; instead, share memory by communicating". I was wondering whether Go uses shared memory or distributed computing approach. For example, for MPI it is clearly distributed, OpenMP is clearly shared memory; but I was not sure about Go, which is unique. I have seen many posts, such as Shared memory vs. Go channel communication, effective Go document etc., but could not clarify. Thanks in advance.

Parallel processing GNU parallel: does -k (keep output order) affect speed?

As said in the title, I'm wondering if the -k option (strongly) affects the speed of GNU parallel. In man parallel_tutorial there is a discussion about --ungroup and --line-buffer, which claims that --linebuffer, which unmixes output lines, is much slower than --ungroup. So maybe -k will also result in major slowdown when the job count is large? (I didn't find this topic in man parallel or man parallel_tutorial; neither did I find anything with some Google. I haven't finished man parallel thou

Parallel processing "Max jobs to run" does not equal the number of jobs specified when using GNU Parallel on remote server?

I am trying to run many small serial jobs with GNU Parallel on a PBS cluster, each compute node has 16 cores, as I intended to use multiple compute nodes therefore I passed the option -S $SERVERNAME to GNUParallel, however what confuses me is that the number of jobs started on the node using -S $SERVERNAME does not equal to the number of jobs I specified when I intended to spawn more than 9 jobs, below are my observations: [fchen14@shelob001 ~]$ parallel --version GNU parallel 20160922 Copyrigh

Parallel processing julia-lang Cache data in a parallel thread using @async

Suppose we have a slow function to produce data and another slow function to process data as follow: # some slow function function prime(i) sleep(2) println("processed $i") i end function slow_process(x) sleep(2) println("slow processed $x") end function each(rng) function _iter() for i ∈ rng @time d = prime(i) produce(d) end end return Task(_iter) end @time for x ∈ each(1000:1002) slow_process(x) end Output: % julia test-task.jl processed 1000 2.06

Parallel processing Render loop - maximum parallelization

Below is a UML sequence diagram showing processing time on my understanding of the game loop in the library libGDX. I think it should be the same architecture for every other game library. I am not sure if I understood it correctly. In theory CPU and GPU work in parallel. When the CPU waits until the GPU is finished for the buffer change this makes it a serial process. How can make my game loop work in parallel or is my understanding wrong? Now image we want to have parallelisation and that th

Parallel processing Proving cut is consistent iff its components are maximum of all the respective components

I gave a qualifier exam recently, there the question was asked to prove the following. In a distributed computation consisting of n processes P1, P2, ...Pn Let C = { c1, c2,· · ·, cn} be a cut where ci is a local event (i.e., neither a message send event nor a message receive event) of process Pi with timestamp VTci (1≤i≤n) respectively. Define VTc = sup (VTc1, VTc2,· · ·, VTcn) where sup is the component-wise maximum operation. i.e.,VTc[i] =max(VTc1[i], VTc2[i],· · ·, VTcn[i]) ∀ i. Then pr

Parallel processing Fortran OMP Parallel Do for a Do While loop

As of lately I have been reading and playing around with OpenMP parallel do's in Fortran 95. However, I still have not figured out how the parallel do would be used in a code like the one beneath: I=1 DO WHILE I<100 A=2*I B=3*I C=A+B SUM(I)=C I=I+1 END DO Using simply !$OMP PARALLEL DO before the do loop and !$OMP END PARALLEL DO doesn't seem to work. I have read a couple of things about private and shared variables however I think that each successive loop of the code above is

Parallel processing There is a increase in execution time every time I increase the number of threads. Shouldn't parallel execution lead to speed-up?

Rows represents the number of elements which were sorted and time is in milliseconds: I have set thread using export OMP_NUM_THREADS=n There is a constant increasing in execution time irrespective of the number of elements I am taking. Where am I going wrong? #include <stdio.h> #include <stdlib.h> #include <time.h> #include "omp.h" /* OpenMP implementation example Details of implementation/tutorial can be found here: http://madhugnadig.com/articles/parallel- processing/2

Parallel processing Forcing SGE to use multiple servers

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),

Parallel processing Apache Airflow: run all parallel tasks in single DAG run

I have a DAG that has 30 (or more) dynamically created parallel tasks. I have concurrency option set on that DAG so that I only have single DAG Run running, when catching up the history. When I run it on my server only 16 tasks actually run in parallel, while the rest 14 just wait being queued. Which setting should I alter so that I have only 1 DAG Run running, but with all 30+ tasks running in parallel? According to this FAQ, it seems like it's one of the dag_concurrency or max_active_runs_p

Parallel processing How to run compression in gnu parallel?

Hi I am trying to compress a file with the bgzip command bgzip -c 001DD.txt > 001DD.txt.gz I want to run this command in parallel. I tried: parallel ::: bgzip -c 001DD.txt > 001DD.txt.gz but it gives me this error: parallel: Error: Cannot open input file 'bgzip': No such file or directory

Parallel processing Most useful parallel programming algorithm?

I recenty asked a question about parallel programming algorithms which was closed quite fast due to my bad ability to communicate my intent: https://stackoverflow.com/questions/2407631/what-is-the-most-useful-parallel-programming-algorithm-closed I had also recently asked another question, specifically: Is MapReduce just a generalisation of another programming principle? The other question was specifically about map reduce and to see if mapreduce was a more specific version of some other con

Parallel processing Recommend method to parallelize a multipass-algorithm

I am writing a de- and encoder for a custom video format (QTC). The decoding process consists of multiple stages, where the output of each stage is passed to the next stage: deserialize the input stream generate a sequence of symbols with a range coder generate a stream of images from the symbol stream serialize the image stream into an output format Steps three and four take up almost all of the processing time, step three takes roughly 35% and step four takes about 60%, the first and the l

Parallel processing Define Asymptotic Run Time of Parallel Algorithm

I am newbie to understanding Parallel Algorithms. Can someone please explain in simple words [or examples] what Asymptotic Run Time of a Parallel Algorithm means? Context: If the best known sequential algorithm for a problem π has an asymptotic run time of S(n) and if T(n,p) is the asymptotic run time of a parallel algorithm, then the asymptotic speedup of the parallel algorithm is defined as S(n)/T(n,p) If S(n)/T(n,p) = Ɵ(p), then the algorithm is said to have linear speed up.

Parallel processing Are there any metrics for both performance and energy efficiency?

For many parallel programs, the parallelization brings substantial cost, making the speedup sublinear. In this case, the parallel versions are less energy efficient than sequential one. However, people may care both the time performance and energy efficiency, are there any specific metrics commonly used for this purpose? More specifically, a metric that can determine the number of threads for best energy and performance goal.

Parallel processing Equal distribution of couchdb document keys for parallel processing

I have a couchdb db instance where each document has a unique id (string). I would like to go over each document in the db and perform some external operation based on the contents of each document (for ex: connecting to another web server to get specific details etc). However, instead of sequentially going over each document, is it possible to first get a list of k buckets of these document keys represented by the starting key + ending key (id being the key), then to query for all documents in

Parallel processing parralel stream syntax prior to java 8 release

Prior to the Java 8 official release, when it was still in development, am I correct in thinking the syntax of getting streams and parallel streams was slightly different. Now we have the option of either saying: stream().parallel() or parallelStream() I remember reading tutorials before its release when there was a subtle difference here - can anyone remind of of what it was as it has been bugging me!

Parallel processing Fortran MPI doesn't run on all of the given number of Processors

I'm currently running a program in which a model grid must be processed. When I want to run the program using eg. 10 processors as workers (mpirun -np 11 -machinefile host civil_mpi.exe), only 3 peocessors run the program and the rest stop at the beginning of the program without any error! If I decrease the size of the model grid, everything works correctly. The total RAM of the machine is over 30 GB, and the size of the Memory needed for each process (based on the model grid size) is less than

Parallel processing Webapplication - ORM or straight functions calls

I know this question must be quite old but I still would like to get a more clear understanding. Imagine you have a Django/RubyOR/PHP application with lets say 200 URLs paths, that would basically also mean you are pointing to 200 (different) functions but also eliminating the layer of abstraction a "ORM conform application" normally comes with, by packing variables into function you maybe don't even really need for that specific user call. Does such a design where you have 200 functio

Parallel processing Why did I get this behaviour?

I was going through the specification of Chapel and was reading on Task Level Parallelism, in particular the synchronization variables (sync and single) and the logical state of them and how they go about. I came across this example given in the specification on this Link var count$: sync int=0; cobegin{ count$+=1 count$+=1 count$+=1 } On running the above code, I get an error but the specification does not talk about it and expects the program to run properly. Why do I get this beh

Parallel processing Segfault when passing data using MPI Winows (MPI_Put)

Good day! I am trying to figure out how to use MPI to work with matrices. Sorry for the very messy and scary code, but I need it to understand every step that occurs during execution. I have a 3 by 6 matrix filled with zeros. I am running code with 3 threads. 0 is the main one. 1 - writes to the first row of my matrix in columns from 1 to 3 ones. 2 stream writes to the second row in columns 4-6 of two. I pass these formed parts to the main thread (at 0), I get the correct result, but after that

Parallel processing How can i connect two or more machines via tcp cable to form a network grid?

How can i connect two or more machines to form a network grid and how can i distribute work load to the two machines? What operating systems do i need to run on the machines, and what application should i use to manage the load balancing? NB: I read somewhere that google uses cheap machines to perform this fete, how do they connect two network cards( 'Teaming' ) and distribute load across the machines? Good practical examples would serve me good, with actual code samples. Pointers to some

Parallel processing MPI parallelism in chaotic systems

I have a Fortran program for dynamics (basically a verlet algo). In order to compute the velocities faster I parallelized the algorithm with MPI. What makes me nervous is that if I have four processors, each processor runs a Verlet, and when they reach a point of parallelization, they share info. However, due to slight numerical differences (for example, in the compiled LAPACK on each node) each Verlet trajectory may evolve in a completely different direction in the long run, meaning that at the

Parallel processing Failure Rate Calculation

I have n identical components which are connected together in parallel system, with each having a failure rate of 0.01. Can someone point me to the equation for calculating the probability that at least 2 component will fail together?

Parallel processing MPI Gather returning incorrect values

I've come to seek help with my issue. The whole below code seems to return proper values for root process, but incorrect values like -1.#IND00 for all other processes. Also Barriers don't work, before I generate the arrays and broadcast them, some of the processes freely go over. The main idea is to put different parts of vector into other processes and then to glue them into one variable with MPI_Gather. I have no idea where I have gone wrong. I'll be grateful for any help given. double *

Parallel processing how to pass list to parallel

I am trying to use parallel in following script #!/bin/bash declare -a ephemeral_list for mount in $(lsblk | grep ^x | awk '{ print $1 }') do if ! mount | grep $mount >/dev/null; then ephemeral_list+=($mount) fi done for i in "${!ephemeral_list[@]}" do printf "%s\t%s\n" "$i" "${ephemeral_list[$i]}" [ -d /mnt/ephemeral$i ] || mkdir /mnt/ephemeral$i mkfs.ext4 -E nodiscard /dev/${ephemeral_list[$i]} && mount /dev/${ephemeral_list[$i]} /mnt/ephemeral$i & done

Parallel processing Mandelbrot optimization in openmp

Well i have to paralellisize the mandelbrot program in C. I think i have done it well and i cant get better times. My question if someone has an idea to improve the code, ive been thinking perhaps in nested parallel regions between the outer and insider for... Also i have doubts if its more elegant or recommended to put all the pragmas in a single line or to write separate pragmas ( one for omp parallel and shared and private variables and a conditional, and another pragma with omp for and sche

Parallel processing how to parallelize M sequential operations over N objects (with sync point)

Suppose I have N objects and M operations (some of which are doing network I/O). I want to call the sequence of operations in order for each of the N objects but allowing parallelism (across the objects) where possible. There is one synch (fan-in) point in the pipeline let's say at operation M-1. What's the best/easiest way to do this in core.async? [Also, this is in ClojureScript so thread is not an option).

Parallel processing OpenMP calling a function gives wrong results

Hi I am trying to put a do loop in different threads. Now inside the do loop I am calling a function which again calls some subroutine and adding to a total sum. Now if I put parallel enclosing the do loop, it is giving random results however I see that if I put the function inside CRITICAL environment it gives the correct result. But this costs more cpu time and does not improve the speed at all. I tested with a small test program and check that my logic is correct. However in a big program (w

Parallel processing error running MPI job on cluster

I am running a code which works perfectly on the cluster, As I increase the number of cores to 3844, I get the following error, "too many retries sending message to 0x0040:0x00152080, giving up" Is this error a network problem? or is this related to the code? I can not post the entire code here unfortunately as it is pretty big Thanks

Parallel processing Calculating the maximum speedup with parallelization

Amdahl's Law lets us calculate the maximum theoretical speedup of a programme when adding more and more processing capacity to our hardware. This is stated by T = 1 / ((1-P) + (P/N)) where (1-P) is the part of the programme that is sequential and (P/N) is the part which can benefit from speedup. Now what Amdahl's law leaves out, is the factor of overhead. To count that in, we can say T = 1 / ((1-P) + 0(N) + (P/N)) where 0(N) represents the synchronization effort that increaes with the increasing

Parallel processing Get Wtime function returning "***"

I'm currently working on converting some Fortran code into parallel using openMP. I'm trying to use omp_get_wtime() to calculate how much actual time passes, but its returning ******. Other OpenMP functions work, yet for some reason this doesn't. I've removed all the code from in between the timer just to try to get something different. Removing the finish, and just displaying the start gives the same result. Any ideas of what I'm doing wrong would be much appreciated. C$ USE OMP_LIB DOUB

Parallel processing What is the difference between POCL(Portable Computing Language) and OpenCL?

What is the difference between POCL(Portable Computing Language) and OpenCL, and what are the advantages POCL? http://pocl.sourceforge.net/ Does POCL have a C-like language, which is different from OpenCL, a different compiler (Clang> = 3.2), different backend (llvm), better portability, or something else? And when we need to use the (hard-linking) OCL, and when need to use the ICD? http://streamcomputing.eu/blog/2013-07-08/installing-and-using-pocl/

Parallel processing How to use pmap on a single large Matrix

I have one very large matrix M (around 5 Gig) and have to perform an operation f: Column -> Column on every column of M. I suppose I should use pmap (correct me if am wrong), but as I understand I should give it a list of matrices. How do I effectively process M in order pass it to pmap? The second question is if it is preferable that f can take multiple columns at once or not.

Parallel processing Varying times during MPI parallel runs

I have written a C++ code for a finite volume solver to simulate 2D compressible flows on unstructured meshes, and parallelised my code using MPI (openMPI 1.8.1). I partition the initial mesh into N parts (which is equal to the number of processors being used) using gmsh-Metis. In the solver, there is a function that calculates the numerical flux across each local face in the various partitions. This function takes the the left/right values and reconstructed states (evaluated prior to the functi

Parallel processing Performance analysis of openmp code (parallel) vs serial code

How do I compare the performance of parallel code(using OpenMP) vs serial code? I am using the following method int arr[1000] = {1, 6, 1, 3, 1, 9, 7, 3, 2, 0, 5, 0, 8, 9, 8, 4, 4, 4, 0, 9, 6, 5, 9, 5, 9, 2, 5, 8, 6, 1, 0, 7, 7, 3, 2, 8, 3, 2, 3, 7, 2, 0, 7, 2, 9, 5, 8, 6, 2, 8, 5, 8, 5, 6, 3, 5, 8, 1, 3, 7, 2, 6, 6, 2, 1, 9, 0, 6, 1, 6, 3, 5, 6, 3, 0, 8, 0, 8, 4, 2, 7, 1, 0, 2, 7, 6, 9, 7, 7, 5, 4, 9, 3, 1, 1, 4, 2, 4, 1, 5, 2, 6, 0, 8, 9, 2, 6, 0, 1, 0, 2, 0, 3, 3, 4, 0, 1, 4, 8, 8, 1, 4, 9, 4

Parallel processing CUDA: do I need different streams on multiple GPUs to execute in parallel?

I want to run kernels on multiple GPUs in parallel. For this purpose I switch between the devices using cudaSetDevice() and then start my kernel in the corresponding device. Now, usually all calls in one stream are executed sequentially and one has to use different streams if they shall be executed in parallel. Is this also the case when using different devices or can I in this case run my kernel calls on the default stream on both devices and they will still run in parallel?

Parallel processing Incremental text file processing for parallel processing

I'm at the first experience with the Julia language, and I'm quite surprises by its simplicity. I need to process big files, where each line is composed by a set of tab separated strings. As a first example, I started by a simple count program; I managed to use @parallel with the following code: d = open(f) lis = readlines(d) ntrue = @parallel (+) for li in lis contains(li,s) end println(ntrue) close(d) end I compared the parallel approach against a simple "serial" one with a 3.5GB f

Parallel processing tensorflow map_fn with tensorflow gather inside not parallelizing

The code I am writing is a bit odd, but rather simple, and I have reduced it to a basic dummy case where the issue pops op. The gist of things is that I have a matrix where elements are actually indices to a second matrix, in this case nearest neighbors, with each row representing a single "query" and each column a neighbor. I need to grab the actual samples that these indices are pointing to, and put them into a 3D tensor (yes it is the intention for duplicates to show up if two "queries" shar

Parallel processing Block reads different length of sequence gpu

I have an array that has different length of sequences, each sequence ended by '>'. seq = [ a,b,f,g,c,d,>,b,g,d,> ....]. I computed the length of each sequence and stored it in a different array called seq_length = [6,3,5,...]. Then, I use the exclusive scan in order to compute the offset and stored it in an array called offset=[0, 6, 9, ..]. What I want is let each block reads a sequence from the array seq[ ] by using the offset value. For example, block 0 reads the sequence that starts from

Parallel processing python3: Run C file and python script in parallel

Ive got a snipped of my actual python script written in this post. Basically I want to have a C programm and a Pyserial function executed in parallel (the C programm is for controlling a motor, the pySerial is for communicating with a arduino). My programm will be executed on a RPi3b using Spyder3 and Rasbipian. What Ive already figured out from the sources below is that if you want to have a terminal program executed in python you should use the subprocess class. If you want to execute somethin

Parallel processing Does @everywhere not load a function on the master?

I made a module with an if condition on the number of cores. If the number of cores is more than 1 the route is parallel; otherwise, it goes the serial route as seen in the code below module mymodule import Pkg using Distributed if nworkers() > 1 @everywhere using Pkg @everywhere Pkg.activate(".") @everywhere Pkg.instantiate() @everywhere using CSV @everywhere include("src/myfuncs.jl") function func() df=CSV.read(file); ....... end else us

Parallel processing Barrier before MPI_Bcast()?

I see some open source code use MPI_Barrier before broadcasting the root value: MPI_Barrier(MPI_COMM_WORLD); MPI_Bcast(buffer, N, MPI_FLOAT, 0, MPI_COMM_WORLD); MPI_Barrier(MPI_COMM_WORLD); I am not sure if MPI_Bcast() already has natural blocking feature. If this is true, I may not need MPI_Barrier() to synchronize the progress of all the cores. Then I can only use: MPI_Bcast(buffer, N, MPI_FLOAT, 0, MPI_COMM_WORLD); So which one is correct?

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