learn how to develop a distributed game of life with dds
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Your systems. Working as one.
May 15, 2013Reinier Torenbeekreinier@rti.com
Learn How to Develop aDistributed Game of Life with DDS
Agenda
• Problem definition: Life Distributed• A solution: RTI Connext DDS• Applying DDS to Life Distributed: concepts• Applying DDS to Life Distributed: (pseudo)-code• Advanced Life Distributed: leveraging DDS• Summary• Questions and Answers
Conway's Game of Life
• Devised by John Conway in 1970• Zero-player game– evolution determined by initial state– no further input required
• Plays in two-dimensional, orthogonal grid of square cells– originally of infinite size– for this webinar, toroidal array is used
• At any moment in time, each cell is either dead or alive• Neighboring cells interact with each other– horizontally, vertically, or diagonally adjacent.
Conway's Game of Life
At each step in time, the following transitions occur:
1. Any live cell with fewer than two live neighbors dies, as if caused by under-population.
2. Any live cell with two or three live neighbors lives on to the next generation.
3. Any live cell with more than three live neighbors dies, as if by overcrowding.
4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.
These rules continue to be applied repeatedly to create further generations.
Conway's Game of Life
Glider gun
Pulsar
Block
Conway's Game of Life – Distributed
Problem description: how can Life be properly implemented in a distributed fashion?• have multiple processes work on parts of the
Universe in parallel
Conway's Game of Life – Distributed
Conway's Game of Life – Distributed
Problem description: how can Life be properly implemented in a distributed fashion?• have multiple processes work on parts of the
Universe in parallel• have these processes exchange the required
information for the evolutionary steps
Conway's Game of Life – Distributed
Conway's Game of Life – Distributed
Problem description: how can Life be properly implemented in a distributed fashion?• have multiple processes work on parts of the
Universe in parallel• have these processes exchange the required
information for the evolutionary steps
This problem and its solution serve as an example for developing distributed applications in general
Conway's Game of Life – Distributed
Properly here means:• with minimal impact on the application logics– let distribution artifacts be dealt with transparently– let the developer focus on Life and its algorithms
• allowing for mixed environments– multiple programming languages, OS-es and hardware– asymmetric processing power
• supporting scalability– for very large Life Universes on many machines– for load balancing of CPU intensive calculations
• in a fault-tolerant fashion
Agenda
• Problem definition: Life Distributed• A solution: RTI Connext DDS• Applying DDS to Life Distributed: concepts• Applying DDS to Life Distributed: (pseudo)-code• Advanced Life Distributed: leveraging DDS• Summary• Questions and Answers
RTI Connext DDS
A few words describing RTI Connext DDS:• an implementation of the Object Management
Group (OMG) Data Distribution Service (DDS)– standardized, multi-language API– standardized wire-protocol– see www.rti.com/elearning for tutorials (some free)
• a high performance, scalable, anonymous publish/subscribe infrastructure
• an advanced distributed data management technology– supporting many features know from DBMS-es
RTI Connext DDS
DDS revolves around the concept of a typed data-space that• consists of a collection of structured, observable
items which– go through their individual lifecycle of creation,
updating and deletion (CRUD)– are updated by Publishers– are observed by Subscribers
• is managed in a distributed fashion– by Connext libraries and (optionally) services– transparent to applications
RTI Connext DDS
DDS revolves around the concept of a typed data-space that• allows for extensive fine-tuning– to adjust distribution behavior according to application
needs– using standard Quality of Service (QoS) mechanisms
• can evolve dynamically– allowing Publishers and Subscribers to join and leave at
any time– automatically discovering communication paths
between Publishers and Subscribers
Agenda
• Problem definition: Life Distributed• A solution: RTI Connext DDS• Applying DDS to Life Distributed: concepts• Applying DDS to Life Distributed: (pseudo)-code• Advanced Life Distributed: leveraging DDS• Summary• Questions and Answers
Applying DDS to Life Distributed
First step is to define the data-model in IDL• cells are observable items, or "instances"– row and col identify their location in the grid– generation identifies the "tick nr" in evolution– alive identifies the state of the cell
module life {
struct CellType { long row; //@key long col; //@key unsigned long generation; boolean alive; };
};
Applying DDS to Life Distributed
First step is to define the data-model in IDL• cells are observable items, or "instances"– row and col identify their location in the grid– generation identifies the "tick nr" in evolution– alive identifies the state of the cell
• the collection of all cells is the CellTopic Topic– cells exist side-by-side and for the Universe– conceptually stored "in the data-space"– in reality, local copies where needed
row: 16col: 4
generation: 25alive: false
Applying DDS to Life Distributed
Each process is responsible for publishing the state of a certain subset of cells of the Universe:• a rectangle or square area with corners
(rowmin,colmin)i and (rowmax,colmax)i for process i
(1,1) – (10,10)
Applying DDS to Life Distributed
Each process is responsible for publishing the state of a certain subset of cells of the Universe:• a rectangle or square area with corners
(rowmin,colmin)i and (rowmax,colmax)i for process i• each cell is individually updated using the
write() call on a CellTopic DataWriter– middleware analyzes the key values (row,col) and
maintains the individual states of all cells• updating happens generation by generation
Applying DDS to Life Distributed
Each process subscribes to the required subset of cells in order to determine its current state:• all neighboring cells, as well as its "own" cells
Applying DDS to Life Distributed
Each process subscribes to the required subset of cells in order to determine its current state:• all neighboring cells, as well as its "own" cells• using a SQL-expression to identify the cells
subscribed to (content-based filtering)– complexity is "Life-specific", not "DDS-specific"
"((row >= 1 AND row <= 11) OR row = 20) AND((col >= 1 AND col <= 11) OR col = 20)"
Applying DDS to Life Distributed
Each process subscribes to the required subset of cells in order to determine its current state:• all neighboring cells, as well as its "own" cells• using a SQL-expression to identify the cells
subscribed to (content-based filtering)– complexity is "Life-specific", not "DDS-specific"
• middleware will deliver cell updates to those DataReaders that are interested in it
Applying DDS to Life Distributed
Additional processes can be added to peek at the evolution of Life:• subscribing to (a subset of) the CellTopic
"row >= 8 AND row <= 13 AND col >= 8 AND col <= 13"
Applying DDS to Life Distributed
Additional processes can be added to peek at the evolution of Life:• subscribing to (a subset of) the CellTopic• using any supported language, OS, platform– C, C++, Java, C#, Ada– Windows, Linux, AIX, Mac OS X, Solaris, INTEGRITY,
LynxOS, VxWorks, QNX…• without changes to the existing applications– middleware discovers new topology and
distributes updates accordingly
Agenda
• Problem definition: Life Distributed• A solution: RTI Connext DDS• Applying DDS to Life Distributed: concepts• Applying DDS to Life Distributed: (pseudo)-code• Advanced Life Distributed: leveraging DDS• Summary• Questions and Answers
Life Distributed (pseudo-)code
Life Distributed prototype applications were developed on Mac OS X• Life evolution application written in C• Life observer application written in Python– using Pythons extension-API
• (Pseudo-)code covers basic scenario only– more advanced apects are covered in next section
Life Distributed (pseudo-)code
Life evolution application written in C:• application is responsible for– knowing about the Life seed (initial state of cells)– executing the Life rules based on cell updates
coming from DDS– updating cell states after a full generation tick has
been processed• evolution of Life takes place one generation at
a time– consequently, Life applications run in "lock-step"
initialize DDScurrent generation = 0write sub-universe Life seed to DDSrepeat repeat wait for DDS cell update for current generation update sub-universe with cell until 8 neighbors seen for all cells execute Life rules on sub-universe increase current generation write all new cell states to DDSuntil last generation reached
Life Distributed (pseudo-)code
Worth to note about the Life application:• loss of one cell-update will eventually stall the
complete evolution– this is by nature of the Life algorithm– implies RELIABLE reliability QoS for DDS– history of 2 generations need to be stored to avoid
overwriting
<dds> <qos_library name="GameOfLifeQosLibrary"> <qos_profile name="CellProfile"> <topic_qos> <reliability> <kind>RELIABLE_RELIABILITY_QOS</kind> </reliability> <history> <kind>KEEP_LAST_HISTORY_QOS</kind> <depth>2</depth> </history> <durability> <kind>TRANSIENT_LOCAL_DURABILITY_QOS</kind> </durability> </topic_qos> </qos_profile> </qos_library></dds>
Life Distributed (pseudo-)code
Worth to note about the Life application:• loss of one cell-update will eventually stall the complete
evolution– this is by nature of the Life algorithm– implies RELIABLE reliability QoS for DDS– history of 2 generations needs to be stored to avoid
overwriting of state of a single cell• startup-order issues resolved by DDS durability QoS
– newly joined applications will be delivered current state– delivery of historical data transparent to applications– applications not waiting for other applications, but for cell
updates
<dds> <qos_library name="GameOfLifeQosLibrary"> <qos_profile name="CellProfile"> <topic_qos> <reliability> <kind>RELIABLE_RELIABILITY_QOS</kind> </reliability> <history> <kind>KEEP_LAST_HISTORY_QOS</kind> <depth>2</depth> </history> <durability> <kind>TRANSIENT_LOCAL_DURABILITY_QOS</kind> </durability> </topic_qos> </qos_profile> </qos_library></dds>
Life Distributed (pseudo-)code
Worth to note about the Life application:• DDS cell updates come from different places– mostly from the application's own DataWriter– also from neighboring sub-Universes' DataWriters– all transparently arranged based on the filter
create DDS DomainParticipantwith DomainParticipant, create DDS Topic "CellTopic"with CellTopic and filterexpression, create DDS ContentFilteredTopic "FilteredCellTopic"create DDS Subscribercreate DDS DataReader for FilteredCellTopic
Life Distributed (pseudo-)code
Worth to note about the Life application:• DDS cell updates come from different places– mostly from the application's own DataWriter– also from neighboring sub-Universes' DataWriters– all transparently arranged based on the filter
• algorithm relies on reading cell-updates for a single generation– evolving one tick at a time– leverages DDS QueryCondition– "generation = %0" with %0 value changing
create DDS DomainParticipantwith DomainParticipant, create DDS Topic "CellTopic"with CellTopic and filterexpression, create DDS ContentFilteredTopic "FilteredCellTopic"with DomainParticiapnt, create DDS Subscriberwith Subscriber and FilteredCellTopic, create DDS CellTopicDataReader
with CellTopicDataReader, query expression and parameterlist, create QueryConditionwith DomainParticipant, create WaitSetattach QueryCondition to WaitSetin main loop: in generation loop: block thread in WaitSet, wait for data from DDS read with QueryCondition from CellTopicDataReader increase generation update query parameterlist with new generation
Life Distributed (pseudo-)code
Life observer application written in Python:• application is responsible for– subscribing to cell updates– printing cell states to display evolution– ignoring any generations that have missing cell
updates
import clifedds as life
#omitted option parsing
filterString = 'row>={} and col>={} and row<={} and col<={}'. format(options.minRow, options.minCol, options.maxRow, options.maxCol)
life.open(options.domainId, filterString)
generation = 0while generation is not None:
# read from DDS, block if nothing availble, # returns Nones in case of time-out after 10 seconds row, col, generation, isAlive = life.read(10)
# omitted administration for building and printing strings
life.close()
Life Distributed (pseudo-)code
Life observer application written in Python:• application is responsible for– subscribing to cell updates– printing cell states to display evolution– ignoring any generations that have missing cell updates
• for minimal impact, DataReader uses default QoS settings– BEST_EFFORT reliability, so updates might be lost– VOLATILE durability, so no delivery of historical updates– still history depth of 2
<dds> <qos_library name="GameOfLifeQosLibrary"> <qos_profile name="CellProfile"> <topic_qos> <history> <kind>KEEP_LAST_HISTORY_QOS</kind> <depth>2</depth> </history> </topic_qos> </qos_profile> </qos_library></dds>
Life Distributed (pseudo-)code
domainId
# of generations
universe dimensions
sub-universe dimensions
Example of running a single life application:
Life Distributed (pseudo-)code
Example of running a life scenario:
Agenda
• Problem definition: Life Distributed• A solution: RTI Connext DDS• Applying DDS to Life Distributed: concepts• Applying DDS to Life Distributed: (pseudo)-code• Advanced Life Distributed: leveraging DDS• Summary• Questions and Answers
Fault tolerance
Not all is lost if Life application crashes• if using TRANSIENT durability QoS,
infrastructure will keep status roaming– requires extra service to run (redundantly)
• after restart, current status is available automatically– new incarnation can continue seamlessly
persistenceservice
Fault tolerance
Not all is lost if Life application crashes• if using TRANSIENT durability QoS,
infrastructure will keep status roaming– requires extra service to run (redundantly)
• after restart, current status is available automatically– new incarnation can continue seamlessly
• results in high robustness• even more advanced QoS-es are possible
Reliability and flow control
Running the Python app with a larger grid:• with current QoS, faster writer with slower
reader will overwrite samples in reader• whenever at least one cell update is missing,
the generation is not printed (by design)
Reliability and flow control
Running the Python app with a larger grid:• whenever at least one cell update is missing,
the generation is not printed (by design)• with current QoS, faster writer with slower
reader will overwrite samples in reader• this is often desired result, to avoid system-
wide impact of asymmetric processing power• if not desired, KEEP_ALL QoS can be leveraged
<dds> <qos_library name="GameOfLifeQosLibrary"> <qos_profile name="CellProfile"> <topic_qos> <reliability> <kind>RELIABLE_RELIABILITY_QOS</kind> </reliability> <history> <kind>KEEP_ALL_HISTORY_QOS</kind> </history> <resource_limits> <max_samples_per_instance>2</max_samples_per_instance> </resource_limits> <durability> <kind>TRANSIENT_LOCAL_DURABILITY_QOS</kind> </durability> </topic_qos> </qos_profile> </qos_library></dds>
Reliability and flow control
Running the Python app with a larger grid:• whenever at least one cell update is missing,
the generation is not printed (by design)• with current QoS, faster writer with slower
reader will overwrite samples in reader• this is often desired result, to avoid system-
wide impact of asymmetric processing power• if not desired, KEEP_ALL QoS can be leveraged– flow control will slow down writer to avoid loss
More advanced problem solving
Other ways to improve the Life implementation:• for centralized grid configuration, distribute grid-
sizes with DDS– with TRANSIENT or PERSISTENT QoS– this isolates configuration-features to one single app– dynamic grid-reconfiguration can be done by re-
publishing grid-sizes• for centralized seed (generation 0) management,
distribute seed with DDS– with TRANSIENT or PERSISTENT QoS– this isolates seeding to one single app
More advanced problem solving
Other ways to improve the Life implementation:• in addition to separate cells, distribute complete sub-Universe
state using a more compact data-type– DDS supports a very rich set of data-types
• bitmap-like type would work well
– especially useful for very large scale Universes– can be used for seeding as well– with TRANSIENT QoS
• multiple Universes can exist and evolve side-by-side using Partitions– only readers and writers that have a Partition in common will interact– Partitions can be added and removed on the fly– Partitions are string names, allowing good flexibility
Agenda
• Problem definition: Life Distributed• A solution: RTI Connext DDS• Applying DDS to Life Distributed: concepts• Applying DDS to Life Distributed: (pseudo)-code• Advanced Life Distributed: leveraging DDS• Summary• Questions and Answers
Summary
Distributed Life can be properly implemented leveraging DDS• communication complexity is off-loaded to
middleware, developer can focus on application• advanced QoS settings allow for adjustment to
requirements and deployment characteristics• DDS features simplify extending Distributed Life
beyond its basic implementation• all of this in a standardized, multi-language, multi-
platform environment with an infrastructure built to scale and perform
Agenda
• Problem definition: Life Distributed• A solution: RTI Connext DDS• Applying DDS to Life Distributed: concepts• Applying DDS to Life Distributed: (pseudo)-code• Advanced Life Distributed: leveraging DDS• Summary• Questions and Answers
Questions?
Thanks!
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