work queue: a scalable master/worker framework peter bui june 29, 2010
Post on 20-Dec-2015
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TRANSCRIPT
Master/Worker Model
• Central Master applicationo Divides work into taskso Sends tasks to Workerso Gathers results
• Distributed collection of Workerso Receives input and executable fileso Runs executable fileso Returns output files
Work Queue versus MPI
Work Queue– Number of workers dynamic– Scale up to large number of
workers (100s - 1000s)– Reliable and fault tolerant at
the task level – Allows for heterogeneous
deployment environments– Workers communicate only
with Master
MPI– Number of workers static– Scale up to limited number of
workers (16, 32, 64)– Reliable at application level
but no fault tolerance– Requires homogeneous
deployment environment– Workers can communicate
with anyone
Architecture (Master)
• Uses Work Queue libraryo Creates a Queueo Submits Tasks
Command Input files Output files
o Library keeps tracks of Tasks When a Worker is available, the
library sends Taskso When Tasks complete
Retrieve output files
Architecture (Workers)
• User start workers on any machine
• Contact Master and request work
• When Task is received, perform commutation, return results
• After set idle timeout, quit and cleanup
API Overview (Work Queue)
Simple C API
• Work Queueo work_queue_create(int port)
Create a new work queue.o work_queue_delete(struct work_queue *q)
Delete a work queue.o work_queue_empty(struct work_queue *q)
Determine whether there are any known tasks queued, running, or waiting to be collected.
API Overview (Task)
Simple C API
• Tasko work_queue_task_create(const char *command)
Create a new task specification. o work_queue_task_delete(struct work_queue_task *t)
Delete a task specification.o work_queue_task_specify_input_file(struct work_queue_task *t, const char *fname, const char *rname);Add input file specification.
o work_queue_task_specify_output_file(struct work_queue_task *t, const char *rname, const char *fname);Add output file specification.
API Overview (Execution)
Simple C API
• Executiono work_queue_submit(struct work_queue *q, struct work_queue_task *t)Submit a job to a work queue.
o work_queue_wait(struct work_queue *q, int timeout)Wait for tasks to complete.
Software Configuration
Web Information
http://cse.nd.edu/~ccl/software/installed.shtml
AFS $ setenv PATH ~ccl/software/cctools/bin:$PATH $ setenv PATH ~condor/software/bin:$PATH
CRC
$ module use /afs/nd.edu/user37/ccl/software/modulefiles $ module load cctools $ module load condor
Example 1: DConvert
• Goal: convert set of input images to specified format in parallelo Input: <format> <input_image1> <input_image2> ...o Output: converted images in specified format
• Skeleton:o ~pbui/www/scratch/workqueue-tutorial.tar.gz
DConvert (Preparation)
Setup scratch workspace$ mkdir /tmp/$USER-scratch$ cd /tmp/$USER-scratch$ pwd
Copy source tarball and extract it$ cp ~pbui/www/scratch/workqueue-tutorial.tar.gz .$ tar xzvf workqueue-tutorial.tar.gz
$ cd workqueue-tutorial$ ls
Open dconvert.c source file for editting$ gedit dconvert.c &
DConvert (TODO 1, 2, and 3)
// TODO 1: include work queue header file
#include "work_queue.h"
// TODO 2: declare work queue and task structs
struct work_queue *q;struct work_queue_task *t;
// TODO 3: create work queue using default port
q = work_queue_create(0);
DConvert (TODO 4, 5, 6)
// TODO 4: create task, specify input and output file, submit task
t = work_queue_task_create(command);work_queue_task_specify_input_file(t, input_file, input_file);work_queue_task_specify_output_file(t, output_file, output_file);work_queue_submit(q, t);
// TODO 5: while work queue is empty wait for task, then delete returned task
while (!work_queue_empty(q)) { t = work_queue_wait(q, 10); if (t) work_queue_task_delete(t);}
// TODO 6: delete work queue
work_queue_delete(q);
DConvert (Demonstration)
Build and prepare application$ make$ cp /usr/share/pixmaps/*.png .
Start batch of workers$ condor_submit_workers `hostname` 9123 5
Start application$ ./dconvert jpg *.png
Tips and Tricks (Debugging)
Debugging
• Enable cctools debugging systemo In master application:
debug_flags_set("wq"); debug_flags_set("debug");
o In workers: work_queue_worker -d debug -d wq <hostname> <port>
• Incrementally test number of workers
Failed Execution
• Include executable and dependencies as input files• Right target platform (32-bit vs 64-bit, OS, etc.)
Tips and Tricks (Tasks)
Tag Tasks
• Give a task an identifying tag so Master can keep track of it
Use input and output buffers• work_queue_task_specify_input_buf
o Contents of buffer will be materialized as a file at worker• task->output
o Buffer that contains standard output of task
Check task results• task->result: result of task• task->return_status: exit code of command line
Tips and Tricks (Batch)
Custom Worker Environment
• Modify batch system specific submit scriptso condor_submit_workers
Set requirementso sge_submit_workers
Set environment Set modules
Tips and Tricks (CRC)
Submit master, find host, submit workers• qsub myscript.sh
#!/bin/cshmaster
• qstat -u <afsid> | grep myscript.sh
• sge_submit_workers <hostname> <port>
Example 2: Mandelbrot Generator
• Goal: generate mandelbrot imageo Input: <width> <height> <xmin> <xmax> <ymin> <ymax> <max_iterations>o Output: mandelbrot image in PPM format
• Skeleton:o ~pbui/www/scratch/workqueue-tutorial.tar.gz
Mandelbrot (Overview)
z(n+1) = z^2 + c
Escape Time Algorithm
• For each pixel (r, c) in image calculate if corresponding point (x, y) escapes boundary
• Iterative algorithm where each pixel computation is independent
Application design
• Master partitions image into tasks• Workers compute Escape Time Algorithm on partitions
Mandelbrot (Naive Approach)
Master
• For each pixel (r, c) in image (width x height)o Computer corresponding x, yo Submit task with for pixel with x, y
Pass x, y parameters as input buffer Tag task with r, c values
• Wait for each task to complete:o Retrieve output of worker from task->outputo Retrieve r, c from task->tago Store pixel[r, c] = output
• Output pixels in PPM format
Mandelbrot (Naive Approach)
Worker
• Read in parameters from input file:o x0, y0, max_iterations, black_value
• Perform Mandelbrot computation as specified from Wikipedia:o http://en.wikipedia.org/wiki/Mandelbrot_set#For_programmers
• Output result (iterations) to standard out
Mandelbrot (Analysis)
Problem
• Processing each pixel as a single task is inefficiento Too-fine grainedo Overhead of sending parameters, running tasks, and
retrieving results > than computation time
Work Queue Golden Rule:
Computation Time > Data Transfer Time + Task setup overhead
Mandelbrot (Better Approach)
Send Rows
• Process groups of pixels rather than individual ones:o Send a row and have the worker return a series of resultso Perhaps send multiple rows?
• Should take execution time from minutes to seconds
Mandelbrot (Demonstration)
Build application$ make
Start batch of workers$ condor_submit_workers `hostname` 9123 10
Start application$ ./mandelbrot_master 512 512 -2 1 -1.5 1.5 250 > output.ppm$ display output.ppm
Advanced Features
Fast Abort
• Allow Work Queue to pre-emptively kill slow tasks• work_queue_activate_fast_abort(q, X)
o X is the fast abort multipliero if (runtime >= average_runtime * X) fast_abort
Scheduling
• Change how workers are selectedo FCFS: first come, first serveo FILES: has the most cached fileso TIME: fastest average turn around time
• Can be set for queue or for task
Advanced Features (More)
Automatic Master Detection
• Start master with a project name:o setenv WORK_QUEUE_NAME="project_name"
• Enable master auto selection mode with workerso work_queue_worker -a -N "project_name"o work_queue_pool -T condor -a -N "project_name"
• Checkout master at http://chirp.cse.nd.edu
Shut down workers• work_queue_shut_down_workers