1. Parallel Data Laboratory, Carnegie Mellon University
Active Disks
Parallel Data Laboratory, Carnegie Mellon University
Storage Systems CSE 598D - Yogesh Sreenivasan
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2. References Active Disks: Programming Model, Algorithms and Evaluation
- Anurag Acharya, Mustafa Uysal and Joel Saltz
Active Disks for Large-Scale Data Processing
- Erik Riedel, Christos Faloutsos, Garth A. Gibson and David Nagle

3. Motivation
Data Storage is growing more rapidly than processor speeds, with this trend processors may not be able to keep up with growing requirements.
CPU Speeds double every 18 months (Moore’s Law)
Customers double their data every 5 months - Greg
With improvements in data transfer rates, even a state-of- the-art processor can keep only a small number of drives busy.
Not able to exploit the parallelism provided by the disks

4. Proposed Solution
Offload bulk of data processing to the disk- resident processors.
Host Processor mainly for coordination, scheduling and combining the results.
Active Disks
Execute application-level functions
directly at the disks.
Send only the results back to the host.
CPU
System Bus
RAM
Disk
Pgm
Host-Resident
Component
Disklets
Active Disks

5. Advantages Leverage Parallelism offered in large storage systems.
More aggregate CPU power in the disks, than in the server.
Reduce interconnect Bandwidth.
Disk based filtering discards fraction of data that would otherwise move across the interconnect.
Appropriate Applications
Execution time dominated by data-intensive core.
Allows parallel implementation of core.
Reasonable selectivity of processing.

6. Architecture Application Program partitioned into, Disklets
Host-resident Component
Disk-resident Component(“Disklets”)
Disklets
Downloaded from host, does major share of processing.
Stream Based Programming model:
Reads input streams from disk
Writes output streams to disk
Optional scratch space.
Disklet cannot initiate I/O operations or allocate free memory.

7. Programming Model
Stream Based Programming model for disklets and their interaction with host-resident peers.
Three types of stream,
Disk-resident streams - Files
Host-resident streams - interact with disklets
Pipe streams - pipe results among disklets.
Communication between a disklet and its environment is restricted to its input and output streams.

8. Disk/Host-Level OS Disk OS Host-level OS
Memory Management - allocation and management of buffers
Stream Communication - possible to overlap data movement and communication
Disklet Scheduling - disklet ready to run whenever there is data available
Host-level OS
Installation of disklets - analyzing disklet code to ensure safety.
Management of host-resident buffers.

9. Algorithms
SQL SELECT
Filters tuples from a relation based on a user-specified predicate.
SELECT id_1, id_2 FROM table_1 WHERE <condition>
Conventional Disk Algorithm
Read all tuples from the disk, keep only those that match the predicate.
Active Disk Algorithm
Disklet reads (on disk) tuples from input stream (file), write matching tuples to output stream. Send only the matching results to the host when finished (or partial results when output stream fills up)

10. Algorithms SQL GROUP-BY
Partitions the relation into disjoint sets of tuples based on the value(s) of index attribute(s).
SELECT MAX(id_1), id_2 FROM table_1 GROUP-BY id_2
Conventional Disk Algorithm
Read all tuples from the disk, accumulate group-by results at the host.
Active Disk Algorithm
Perform local group-by at the disk, send the final vector of aggregates.
Ship partial results, when out of space.

11. Algorithms External sort Datacube Image convolution
To sort database tuples when main memory is not enough to cache all the records.
Datacube
General form of aggregation for relational databases (multidimensional aggregation).
Perform GROUP-BY on multiple indexes.
Image convolution
Image processing operations like edge detection, gradient detection, smoothing and blurring etc.
Generating composite satellite images
Generate earth images by compositing satellite-based data acquired over multiple days.

12. Evaluation Experiments conducted for two configurations
Systems that can be purchased at that time.
Systems that are likely to be available by the end of the decade.
Experiments performed with number of disks varying from 4 to 32 to explore scalability.

13. Performance Utility of Active Disks
Performance improvements of 1.07 to 3.15 times for Current configurations.
Performance improvements of 1.18 to 3.2 times for predicted future configurations.

14. Performance Variation in interconnect bandwidth
Active Disks outperforms conventional disks for all configurations.
Algorithms with significant computation per byte transferred cannot take advantage of faster interconnect.

15. Performance Scalability
For conventional disk architectures, increasing the number of disks doesnot have any advantage.
Processor can keep only a small number of drives busy
Active disks scale well up to 16 disks

16. Performance Impact of host processor
For large configurations active disk architecture outperforms conventional disks architectures.
Disk processor is assumed to be 200MHz
For sort algorithm, performance improves with increase in host processor speed for active disk architectures.

17. Performance Model Host saturation - interconnect or server bottleneck
Disk saturation - raw disk limitation
Disk-CPU saturation - active disks CPU limitation
Transfer saturation - active disks saturate their interconnect

18. Additional Applications
Nearest neighbor search
Determines k items in a database that are closest to a particular input item.
Target record is sent to each of the disks, and each disk returns the k closest records in its portion.
Server combines the results to find the overall k closest records.
Edge Detection
Active disk system performs edge detection for each image directly at the drives and returns only the edges to the server.
Image Registration
Find rotation and translation from a reference image.
Similar to edge detection.

19. Performance
With more disks, active disk systems achieves a much higher throughput.
Even in CPU-bound tasks, active disks show linear or near linear improvement with increase in number of disks.

20. Conclusion Active Disk systems, Can improve throughput in,
Offer parallelism available in large storage systems
Dramatically reduces interconnect bandwidth
Can improve throughput in,
Data mining and multimedia applications
Applications that can leverage parallelism and selectivity.
Active Disks or PC Clusters?