Creating Better Force Fields on Distributed GPUs with Work Queue

ForceBalance is an open source software tool for creating accurate force fields for molecular mechanics simulation using flexible combinations of reference data from experimental measurements and theoretical calculations. These force fields are used to simulate the dynamics and physical properties of molecules in chemistry and biochemistry.

The Work Queue framework gives ForceBalance the ability to distribute computationally intensive components of a force field optimization calculation in a highly flexible way. For example, each optimization cycle launched by ForceBalance may require running 50 molecular dynamics simulations, each of which may take 10-20 hours on a high end NVIDIA GPU. While GPU computing resources are available, it is rare to find 50 available GPU nodes on any single supercomputer or HPC cluster. With Work Queue, it is possible to distribute the simulations across several HPC clusters, including the Certainty HPC cluster at Stanford, the Keeneland GPU cluster managed by Georgia Tech and Oak Ridge National Laboratories, and the Stampede supercomputer managed by the University of Texas. This makes it possible to run many simulations in parallel and complete the high level optimization in weeks instead of years.

 - Lee-Ping Wang, Stanford University

CCTools 4.3 released

The Cooperative Computing Lab is pleased to announce the release of version 4.3.0 of the Cooperative Computing Tools, including Parrot, Chirp, Makeflow, WorkQueue, Weaver, DeltaDB, SAND, All-Pairs, and other software. This release has some important changes:

• Peter Bui's Weaver is included. Weaver is a high level interface to Makeflow which allows to describe workflows using python. For more information see cctools/doc/man/weaver.1 and cctools/weaver/examples in the distribution.
• This is also the first release to include DeltaDB, written by Peter Ivie and Douglas Thain. DeltaDB implements a model for time-varying schema-free data and underlies the query engine for the CCTools catalog server.
• Backwards compatibility of master and workers pre-4.3 is broken. Workers from 4.3 cannot connect to masters pre-4.3, and masters from 4.3 will not accept connection from workers pre-4.3. The API did not change, thus unless you want to take advantage of new features, you should not need to modify your code.
• The interface to work_queue_pool has been simplified, and all options have to be specified at the command line. Please see cctools/doc/man/work_queue_pool.1 for more information.
• Undefined environment variables used in Makeflow are no longer allowed by the parser.
• Binaries for 32bit architectures are not being distributed as part of this release. Please let us know if you need them.

Other highlights

• [WorkQueue] Perl object oriented bindings have been added. See perldoc Work_Queue::Queue [B. Tovar]
• [WorkQueue] A priority per task can now be specified. [D. Thain, B. Tovar]
• [WorkQueue] --single-shot option added to workers to exit quickly after the master disconnects [D. Thain].
• [WorkQueue] Hierarchy statistics when using foremen are now available. [B. Tovar, M. Wolf]
• [WorkQueue] work_queue_pool code cleanup. [D. Thain, B. Tovar]
• [Makeflow] New lexer and parser with cleaner semantics and error reporting. [B. Tovar]
• [Parrot] Bug fix that allows parrot's temp-dir to be on GPFS. [P. Donnelly]
• [Parrot] Several fixes to better support executables with threads. [P. Donnelly]
• [Parrot] Update to use the newer ptrace API. [P. Donnelly]
• [Parrot] Several updates to parrot_package_run. See cctools/doc/man/parrot_package_run. [H. Meng]
• [Parrot] iRODS 4.x support. [D. Thain]

You can download the software here: cctools download

Thanks goes to the contributors and testers for this release: Peter Bui, Patrick Donnelly, Nick Hazekamp, Peter Ivie, Kangkang Li, Haiyan Meng, Peter Sempolinski, Douglas Thain, Ben Tovar, Lee-Ping Wang, Matthias Wolf, Anna Woodard, and Charles Zheng

Enjoy!

Work Queue Powers Nanoreactor Simulations

Lee-Ping Wang at Stanford University, recently published a paper in Nature Chemistry describing his work in fundamental molecular dynamics.

The paper demonstrates the "nanoreactor" technique in which simple molecules are simulated over a long time scale to observe their reaction paths into more complex molecules.  For example, the picture below shows 39 Acetylene molecules merging into a variety of hydrocarbons over the course of 500ps simulated time.  This technique can be used to computationally predict reaction networks in historical or inaccessible environments, such as the early Earth or the upper atmosphere.

To compute the final reaction network for this figure, the team used the Work Queue framework to harness over 300K node-hours of CPU time on the Blue Waters supercomputer at NCSA.

Open Sourcing Civil Engineering with a Virtual Wind Tunnel

In addition to the CCL tools themselves, members of the CCL lab often collaborate with other research groups to help them solve their scientific problems, using collaborative computing. Often, such collaborative projects drive the development and debugging of our tools.

An uploaded design in the Virtual Wind Tunnel

One such project is a Virtual Wind Tunnel, which was created in collaboration with the Notre Dame Civil Engineering Department, as part of a larger project to explore collaboration in civil design. On the surface, this is a fairly simple idea. A user uploads a building shape for analysis to a web portal. Then, the user can run wind flow simulations upon horizontal cross sections of the building. Once complete, the results of these simulations can be viewed and downloaded.

Making all of this work, however, requires a large number of interlocking components. For now, I would just like to describe how the CCL tools play a role in this system. When simulations are to be run, one very simple way to deliver simulation tasks to available computing resources is to run a Work Queue worker on those machines. The front-end of the system runs a Work Queue master, which queues up tasks.

Viewing Results of a Simulation

This has several advantages, but the most important is that we can be flexible about the resources which we use at any given time, even using computing resources from multiple sources at the same time. For example, we have a small private cloud which we use for experimental purposes. We also have access to an on-campus SGE grid, but must share with many other customers. Our current approach is to set up a handful of VM on the private cloud, which run workers. If demand for simulations is high enough, we ask for more workers from the SGE.

By using Work Queue as a means of distributing tasks, we can be more flexible about the backend upon which those tasks are run. This allows us to tailor our resource usage to our actual needs and, as needed, to adjust our resource usage when appropriate.

DeltaDB - A Scalable Database Design for Time-Varying Schema-Free Data

DeltaDB is a log-structure database and query model designed for time-varying and schema-free data. The following video gives a high level overview of DeltaDB and describes how the model is scalable using MapReduce.

This database design is implemented within CCTools in two parts. Part 1 (data storage) has been available for over a year and is called the catalog server. Part 2 (data analysis) has recently been implemented and is not yet in a release, but is available in the following commit:

https://github.com/pivie/cctools/commit/bca998baf00c71484b567110d73c36bd042c3b3e

The data model is designed to handle schema-free status reports from various services. And while the reports can be schema-free, most of the fields will normally remain the same between subsequent reports from the same instance of a service.






The first status report is saved in it's entirety, and then the subsequent reports are saved as changes (or "deltas") on the original report. Snapshots of the status of all services and instances are stored on a daily basis. This allows a query for analysis based on a given time frame to jump more quickly to the start of the time frame, rather than have to start at the very beginning of the life of the catalog server.




A query is performed by applying a series of operators to the data. For a distributed system, spatial distribution is when the data is distributed such that a given instance always ends up on the same node. In this situation, all but the last of the operators can be performed in the map stage of the MapReduce model. This allows for better scalability because less work has to be performed by a single node in the reduce stage.

Much more detail is provided in a paper which was published at IEEE Bigdata 2014, and is available at the following URL:

http://ccl.cse.nd.edu/research/papers/pivie-deltadb-2014.pdf

For further inquiries, please email pivie@nd.edu.

Packaging Applications with Parrot 4.2.0

CCTools 4.2.0 includes a new feature in Parrot that allows you to automatically observe all of the files used by a given application, and then collect them up into a self-contained package.  The package can then be moved to another machine -- even a different variant of Linux -- and then run correctly with all of its dependencies present. The created package does not depend upon Parrot and can be re-run in a variety of ways.
  
This article explains how to generate a self-contained package and then share it so that others can verify can repeat your applications. The whole process involves three steps: running the original application, creating the self-contained package, and the running the package itself.

Figure 1 Packaging Procedure

Step 1: Run the original program

Run your program under parrot_run and record the filename list and environment variables by using --name-list and --env-list parameters.

parrot_run --name-list namelist --env-list envlist /bin/bash
 
After the execution of this command, you can run your program inside parrot_run.  At the end of step 1, one file named namelist containing all the accessed file names and one file named envlist containing environment variables will be generated.  After everything is done, simple exit the shell.

Step 2: Generate a self-contained package

Use parrot_package_create to generate a package based on the namelist and envlist generated in step 1.

parrot_package_create --name-list namelist --env-path envlist --package-path /tmp/package
 
This command causes all of the files given in the name list to be copied into the package directory /tmp/package.  You may customize the contents of the package by editing the namelist or the package directory by hand.

Step 3: Repeat the program using the package

The newly created package is simply a complete filesystem tree that can be moved to any convenient location.  It can be re-run by any method that treats the package as a self-contained root filesystem.  This can be done by using Parrot again, by setting up a chroot environment, by setting up a Linux container, or by creating a virtual machine.

To run the package using Parrot, do this:

parrot_package_run --package-path /tmp/package /bin/bash 

To run the package using chroot, do this:

chroot_package_run --package-path /tmp/package /bin/bash

In both cases, you will be dropped into a shell in the preserved environment, where all the files used by the original command will be present.  You will definitely be able to run the original command -- whether you can run other programs depends upon the quantity of data preserved.

For more information, see these man pages:

• parrot_run
• parrot_package_create
• parrot_package_run
• chroot_package_run

CCTools 4.2.0 released

We are pleased to announce the release of version 4.2.0 of the Cooperative Computing Tools including Parrot, Chirp, Makeflow, WorkQueue, SAND, All-Pairs, and other software.
The software may be downloaded here: Download CCTools 4.2.0
This release is mostly a bug fix release, but introduces changes to the Work Queue protocol. Thus, workers from 4.2 do not work with masters pre 4.2.
Among the bug fixes and added capabilities are: Among the bug fixes and added capabilities are:

• [General] Support for systemd log journal. (Patrick Donelly)
• [WorkQueue] Several bug fixes (Douglas Thain. Dinesh Rajan, Ben Tovar)
• [WorkQueue] Improvements to resource accounting. (Ben Tovar)
• [WorkQueue] work_queue_graph_log, a script to plot Work Queue's log. (Ben Tovar)
• [WorkQueue] Autosize option for workers to fill Condor slots. (Douglas Thain)
• [WorkQueue] Added several example applications in apps/ (Dinesh Rajan)
• [Chirp] Several bug fixes. (Patrick Donelly)
• [Parrot] Package creation of accessed files for execution repeatability. (Haiyan Meng)
• [Parrot] Correct mmap handling. (Patrick Donelly)
• [Parrot] Fix linking to iRODS. (Patrick Donelly)
• [Parrot] Option to disable CVMFS alien cache. (Ben Tovar)
• [Parrot] Bug fixes targeting CVMFS. (Ben Tovar)

Thanks goes to the contributors for many features and bug fixes:

• Jakob Blomer
• Dan Bradley
• Peter Bui
• Patrick Donnelly
• Nicholas Hazekamp
• Peter Ivie
• Haiyan Meng
• Dinesh Rajan
• Casey Robinson
• Peter Sempolinski
• Douglas Thain
• Ben Tovar
• Matthias Wolf

Please send any feedback to the CCTools discussion mailing list .

DeltaDB at IEEE BigData 2014

Peter Ivie will be presenting his work on the DeltaDB database model for time-varying schema-free data at the IEEE International Congress on Big Data in Anchorage. The DeltaDB concept is what underlies the query engine for the cctools catalog server.

Journal Paper on MAKER and Work Queue

Our paper on converting the MAKER bioinformatics analysis from MPI to Work Queue, done in collaboration with the Notre Dame Bioinformatics Laboratory was recently accepted for publication in the International Journal of Bioinformatics Research and Applications.

CCL Papers at CCGrid 2014

Three papers from the CCL were presented at the 2014 IEEE Conference on Cluster, Cloud, and Grid Computing in Chicago:

• Paul Brenner presented a regular paper,
  Opportunistic High Energy Physics Computing in User Space with Parrot which shows how we use Parrot to provide transparent access to complex software stacks without requiring a dedicated shared file system.
• Olivia Choudhury presented Accelerating Comparative Genomics Workflows in a Distributed Environment with Optimized Data Partitioning at the C4Bio Workshop. This paper explores various strategies for data partitioning in bioinformatics applications; showing that partitioning for parallel execution must be specialized to the objective of the underlying tool.
• Nick Hazekamp presented a poster, Expanding Tasks of Logical Workflows into Independent Workflows for Improved Scalability in which he demonstrated a technique for transparently parallelizing components of a Galaxy workflow using Makeflow.

Toward a Common Model of Highly Concurrent Programming

(This is the short version of a talk I gave at the MTAGS workshop at Supercomputing 2013.  See the slides here.)

Historically, highly concurrent programming has been closely associated with high performance computing.  Two programming models have been dominant: shared memory machines in which concurrency was expressed via multiple threads, and distributed memory machines in which concurrency was expressed via explicit message passing.  It is widely agreed that both of these programming models are very challenging, even for the veteran programmer.  In both cases, the programmer is directly responsible for designing the program from top to bottom and handling all of the issues of granularity, consistency, and locality necessary to achieve acceptable performance, with very little help from the runtime or operating systems.

However, a new approach to concurrent programming has been emerging over the last several years, in which the user programs in a much higher level language and relies upon the system to handle many of the challenging underlying details.  To achieve this, the program is successively decomposed into simpler representations, such that each layer of the system can gradually adapt it to the hardware available.

The layers can be described as follows:

• A declarative language (DL) for compactly representing a complete program.
• A directed graph (DAG) to represent the expanded program and its resources.
• A bag of independent tasks (BOT) with explicit input and output dependencies.
• A shared-nothing cluster to which data and tasks must be assigned.

Several different research communities have arrived at this computing model somewhat independently: the high performance computing community, the scientific workflow community, and the cloud computing community.  In each case, the scale and complexity of the systems in use eventually made it impossible for the programmer or the user to take responsibility for all of the challenges of parallel/distributed computing.  Although each community employs different technologies and has distinct optimization goals, the overall structure of these systems is surprisingly similar.

A (very incomplete) selection of systems that follow this model:

Layer	Cloud Stack	Workflow Stack	HPC Stack
Declarative Language (DL)	Pig	Weaver	Swift-T
Directed Acyclic Graph (DAG)	Map-Reduce	Makeflow	-
Bag of Tasks (BOT)	JobTracker	Work Queue Master	Turbine
Distributed Data	HDFS	Work Queue Workers	MPI

Each layer of the system fulfills a distinct need.  The declarative language (DL) at the top is compact, expressive, and easy for end users, but is intractable to analyze in the general case because it may have a high order of complexity, possibly Turing-complete.  The DL can be used to generate a (large) directed acyclic graph (DAG) that represents every single task to be executed.  The DAG is not a great user-interface language, but it is much more suitable for a system to perform capacity management and optimization because it is a finite structure with discrete components.  A DAG executor then plays the graph, dispatching individual tasks as their dependencies are satisfied.  The BOT consists of all the tasks that are ready to run, and is then scheduled onto the underlying computer, using the data dependencies made available from the higher levels.

Why bother with this sort of model?  It allows us to compare the fundamental capabilities and expressiveness of different kinds of systems.  For example, in the realm of compilers, everyone knows that a proper compiler consists of a scanner, a parser, an optimizer, and a code generator.  Through these stages, the input program is transformed from a series of tokens to an abstract syntax tree, an intermediate representation, and eventually to assembly code.  Not every compiler uses all of these stages, much less the same code, but by using a common language, it is helpful to understand, compare, and design new systems.

Visualizing 10,000 Cores in Condor

Our Condor pool at the University of Notre Dame has been slowly growing, in no small part due to our collaboration with the Center for Research Computing, where it is now scavenging unused cycles from HPC clusters at the CRC.  When the dedicated batch system leaves a node unused, Condor is started on that node and keeps going until the dedicated system wants the node back.  Depending on the time of year, that leaves anywhere between 4K and 10K nodes available in the Condor pool.

We have tried a number of approaches at visualizing this complex system over the years.  Our latest tool, the Condor Matrix Display started as a summer project by Nick Jaeger, a student from the University of Wisconsin at Eau Claire.  The display shows a colored bar for each slot in the pool, where the width is proportional to the number of cores.

With a quick glance, you can see how many users are busy and whether they are running "thin" (1 core) or "fat" (many core) jobs.  Sorting by the machine name gives you sense of how each sub-cluster in the pool is used:

While sorting by users gives you a sense of what users are dominating the pool:

The display is always a nice way of viewing the relatively new feature of "dynamic slot" in Condor.  A large multi-core machine is now represented as a single slot with multiple resources.  For example, this bit of the display shows a cluster of 8-core machines where some of the machines are unclaimed (green), some are running 4-core jobs (blue), and some are running 1-core jobs (green):