1. Adopting OpenCAPI for High Bandwidth Database Accelerators
Authors: Jian Fang1, Yvo T.B. Mulder1, Kangli Huang1, Yang Qiao1,
Xianwei Zeng1, Jan Hidders2, Jinho Lee3, H. Peter Hofstee1,3
SC’17, Denver, USA
Speaker: Jian Fang
November 17th, 2017

2. Netezza Data Appliance Architecture
Source: The Netezza data appliance architecture:
A platform for high performance data warehousing and analytics

3. S-Blade in Netezza Decompressed Data Compressed Data Filtered Data
Source: The Netezza data appliance architecture:
A platform for high performance data warehousing and analytics

4. Netezza Data Appliance Architecture
There are also some commercial products shown the possibility of doing this like Kickfire and Netezza. In Netezza, FPGAs are placed in the data path to perform as a decompress-filter, increasing the disk bandwidth indirectly. The improvement comes from solving the disk bandwidth limitatoin.
Bottleneck
Source: The Netezza data appliance architecture:
A platform for high performance data warehousing and analytics

5. DB with FPGAs: What is new?
Databases move from Disk to Memory
Databases move from Disk to Flash
FPGAs still
help? √
Faster Data
Movement
Neo4j Touts 10x Performance Boost of Graphs on IBM Power FPGAs
FADS: Failed Aspiration on Database System
Source:
neo4j-touts-10x-performance-boost-of-graphs-on-ibm-power-fpgas/

6. OpenCAPI Helps High Bandwidth Low Latency Shared Memory
100GB/s in total with 4 Channels
OpenCAPI 3.0
High Bandwidth
OpenCAPI brings FPGAs memory scale bandwidth
OpenCAPI 3.0(x8) -> 25GB/s
OpenCAPI 4.0(x32) -> 100GB/s
Shared memory
Address Translation
Save extra copies
Target on more than computation-intensive applications
OpenCAPI 4.0
Low Latency
100GB/s in total with 1 Channel
OpenCAPI 4.0
OpenCAPI brings FPGAs memory scale bandwidth
Target more than computation-intensive applications
Shared Memory
Source:

7. Acceleration DBs with OpenCAPI
Decompress-Filter
Hash-Join
Merge-Sorter
• • •
Each has different buffering requirements, which are challenging at this speed. Requirements vary over having to hide latency versus the number of read ports.

8. Do we need compression & decompression?
Decompress-Filter
Do we need compression & decompression?
Parquet format
Partitionable
Supports GZIP, LZO, Snappy, ...
Snappy (de)compress algorithm
Based on LZ77, byte-oriented
Low compress ratio, but fast (de)compress speed
Computation-bound
Highly data dependent
Multiple engines to keep up the bandwidth
Trade off between stronger but fewer engines and simpler but more engines (64KB history for each engine)
Memory access pattern
Sequential read for each stream (engine)
Each has different buffering requirements, which are challenging at this speed. Requirements vary over having to hide latency versus the number of read ports.

9. Hash-Join Memory-bound Memory access pattern 40% Waste
Low locality of the data and multiple passes of data transfers
The internal memory (BRAM) is too small to store the hash table
Memory access pattern
Sequentially read the relations
Randomly write/read the hash table
Granularity matters during random accesses
Each has different buffering requirements, which are challenging at this speed. Requirements vary over having to hide latency versus the number of read ports.
40%
Waste
Require: 40B tuple
Access : 64B cacheline
Wasted: 24B
Fang J, et al. Analyzing In-Memory Hash Joins: Granularity Matters, ADMS 2017.

10. Merge-Sorter Need strong sorter for the final pass
Memory access pattern
Sequentially read within each stream, but randomly choose between streams
Solutions
Even-odd sorter to continuously produce multiple tuples per cycle
Multi-stream bufferring to feed this beast
Each has different buffering requirements, which are challenging at this speed. Requirements vary over having to hide latency versus the number of read ports.
Q(N)
10,15,11,16,13,14,12,9
3,5,6,1,8,4,2,7 Q3 Q4
Odd Cycle
OUTPUT
1,2,3,4,5,6,7,8
9,10,11,12,13,14,15,16
Even Cycle
INPUT

11. Summary Databases have/need faster rate moving data
With OpenCAPI, FPGAs can help DBs more
Challenges of high bandwidth acclerator design
Three examples
You can use OpenCAPI by join OpenPOWER community.

12. Vrije Universiteit Brussel
Authors
Jian Fang
TU Delft
Xianwei Zeng
Yvo T.B. Mulder
Jan Hidders
Vrije Universiteit Brussel
Jinho Lee
IBM Research
Kangli Huang
H. Peter Hofstee
TU Delft & IBM Research
Yang Qiao
You can use OpenCAPI by join OpenPOWER community.

13. Thank You More Detail: Progress with Power Systems and CAPI
Leveraging the bandwidth of OpenCAPI with reconfigurable logic
Contact Me: