Алексей Рагозин, Technical Lead, Caching and Data Grid Services, VP, Risk and PnL, Deutsche Bank

1. High Performance Computing
Cloud point of view
Alexey Ragozin
alexey.ragozin@gmail.com
Sep 2012

2. Massive parallel computing
 I/O bound workload
• Data mining / machine learning / indexing
• Focus: Do not move data, process in place
 CPU bound
• complex simulations / complex math models
• Focus: Keep all cores busy
 Latency bound
• Physical process simulations
(e.g. weather forecast)
• Focus: Minimize communication latencies

3. CPU bound task
 Stream of independent tasks
• Independent tasks
• Random continuous stream of tasks
• E.g. video conversion, crawling
 Structured batch jobs
• Single batch is split into subtasks for parallel execution
• Task may have data dependency on each other
• Task may be generated during batch execution
• E.g. portfolio risk calculation

4. Handling task stream in Cloud
Worker pool
incoming
in g Task queue
p oll tasks
queue metrics
Controler
adjusts pool size
based on queue metrics
Simple pattern. Exploiting “elasticy” of cloud. Cost effective.

5. Structured batch jobs in cloud
Batches are usually more sporadic
 e.g. end of day risk calculations
Task may have cross dependencies
 scheduler should be “cloud-aware”
Supplying tasks with data
 data delivery delay is critical
 worker pool is generally very large
 data sets also could be very large

6. Data delivery strategy
Push approach
 scheduler controls data delivery
 worker expects data to be available locally
 more opportunities for optimization
 complex
Pull approach
 worker pulls required data from central service
 scheduler is unaware about data sets
 requires scalable data service
 much simpler

7. What kind of data do we have?
Working set
• working set is divided between jobs
• each portion of working set processed by single job
• often jobs are producing working set for next
computation stage
Reference data
• exactly same data shared by multiple/all jobs
• usually static data set

8. Data distribution problem
Working set
• Spiky work load – especially at the start
• Hard to predict there piece of data will be required
• Caching is ineffective
Reference data set
• Naïve approach will produce huge volume of
redundant transfers – smart caching required
• Spiky work load

9. Private grid practice
HPC Grid
RDBMS
or
Data Warehouse
Data grid

10. Data grid, what is it?
• Key/Value storage
• Data distributed across cluster of servers
• RAM is usually used as storage
• Redundant copies provide level of fault tolerant /
durability
• No single point of failure
• Automatic rebalancing of data when servers
added/removed from grid
• Capacity and throughput are scaling linearly

11. Data service for cloud HPC
• Block storage service
Azure drive / Amazon EBS
– Lack of shared access to data
• Key / Value storage
Azure Tables / Amazon Simple DB
– Pricing: volume + usage
• Blob store
Azure Tables (blobs) / Amazon S3
– Pricing: volume + transactions
– Good read scalability

12. Use case for caching
 Avoid storage of data in cloud
• Upload data once per batch and cache in cloud
 Reduce storage cost by reducing number of
operations
 Save IO bandwidth for shared data
• Edge caching
• Routing overlays

13. Distribution tree /
Routing overlays
Storage
Proxy
Clients
Switch Switch Switch

14. Task stealing
Task steeling – alternative scheduling approach
Task steeling in widely used for in-process multi-core concurrency
Why use it for cluster task scheduling?
• Stochastic and adaptive
• Can use cost models accounting internal cloud
topology
• Decently solves problem of data delivery,
without additional caching
• Unproven for cluster computation, so far

15. Task stealing
Worker 1
 Work backlog is organized in a
form of stack
 Tasks are generated recursively
 Top of stack – fine grained tasks
fork  Bottom of stack – coarse
grained tasks
fork  Execution from top of stack
fork
 Stealing – bottom of stack
processing

16. Task stealing
Worker 1 Worker 2
steal
fork
fork
processing
fork
fork
processing
fork
done

17. IO bound workload in cloud
Dawn of Map/Reduce
- high bandwidth interconnects are expensive
- network storage is expensive (due to network cost)
- cheap serves and local processing for keeping costs low
- price – very complex computation model
“Cloud” reality
- network bandwidth is cheap
- disks are already “networked”
- RAM is abundant

18. Hadoop is cloud unfriendly
Assume I have 50 nodes Hadoop cluster in cloud
What will I gain by adding another 50 nodes?
- Not much, until they are populated with data.
What if I will shut these 50 afterward?
- Effort to populate them with data will be wasted.
Hadoop is coupling execution and storage services
together – you have pay for both even if you use one.

19. How cloud M/R should look?
• Use cloud storage service and persistent storage
• Streaming M/R processing
• Aggressive use of memory for intermediate data
Peregrine – storeless M/R framework
http://peregrine_mapreduce.bitbucket.org/
Spark – in-memory M/R framework
http://www.spark-project.org/

20. Looking into future
Highly anticipated features
 Scheduler as a Service
Azure HPC / Amazon SWF
 Simple middleware for organizing caches and
routing overlays
Existing solutions are far from simple
 Cloud friendly map/reduce frameworks
Could provider work hard to offer effective Hadoop

21. Thank you
http://blog.ragozin.info
- my articles
Alexey Ragozin
alexey.ragozin@gmail.com