Work Queue: A Scalable Master/Worker Framework Peter Bui June 29, 2010

Master/Worker Model Central Master application o Divides work into tasks o Sends tasks to Workers o Gathers results Distributed collection of Workers o Receives input and executable files o Runs executable files o Returns output files

Work Queue versus MPI Work Queue – Number of workers dynamic –Scale up to large number of workers (100s s) –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

Success Stories Makeflow SAND Wavefront All-Pairs

Architecture (Overview)

Architecture (Master) Uses Work Queue library o Creates a Queue o Submits Tasks  Command  Input files  Output files o Library keeps tracks of Tasks  When a Worker is available, the library sends Tasks o 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 Queue o 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 Task o 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 Execution o 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 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 parallel o Input:... 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` Start application $./dconvert jpg *.png

Tips and Tricks (Debugging) Debugging Enable cctools debugging system o In master application:  debug_flags_set("wq");  debug_flags_set("debug"); o In workers:  work_queue_worker -d debug -d wq 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 scripts o condor_submit_workers  Set requirements o sge_submit_workers  Set environment  Set modules

Tips and Tricks (CRC) Submit master, find host, submit workers qsub myscript.sh #!/bin/csh master qstat -u | grep myscript.sh sge_submit_workers

Example 2: Mandelbrot Generator Goal: generate mandelbrot image o Input: 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, y o 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->output o Retrieve r, c from task->tag o 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 Output result ( iterations ) to standard out

Mandelbrot (Analysis) Problem Processing each pixel as a single task is inefficient o Too-fine grained o 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 results o 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` Start application $./mandelbrot_master > 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 multiplier o if (runtime >= average_runtime * X ) fast_abort Scheduling Change how workers are selected o FCFS: first come, first serve o FILES: has the most cached files o 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 workers o work_queue_worker -a -N "project_name" o work_queue_pool -T condor -a -N "project_name" Checkout master at Shut down workers work_queue_shut_down_workers

Web Resources Website User manual and C API documentation Bug Reports and Suggestions Python-API Experimental Python binding