http://lidarnews.com/breaklines-and-lidar-101

1. High Performance
Computing for LiDAR
Data Production
March 3, 2010

2. Processor Technologies and Speeds
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3. 64-bit Computing
 Requirements
 64-bit compatible hardware
 64-bit operating system
 64-bit designed software (not the same as 32-bit software that will run
on a 64-bit computer)
 Benefits
 Access to larger amounts of memory, 32-bit OS can only read 4 GB of
RAM (maximum accessible RAM is closer to 3.4 GB due to other
hardware limitations)
 More data can be read in at once
 More RAM space available for multiple processes requiring memory space
 More stable
 Less memory management needed to prevent out of memory errors
 32-bit operating systems limit the kernel-mode virtual address space to 2
GB, the 64-bit limit is 8 TB
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4. CPU Trends
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5. Use of Multiple Processors and Large Amounts of RAM
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6. Multi-Core / Multi-Threaded Processing
MARS® Auto-Filter Benchmark Results
Multiprocessing with
16 processors yielding
a 91% time savings
4:48:00
4:19:12
3:50:24
3:21:36
HH:MM:SS
2:52:48
4:31:59
2:24:00
1:55:12
1:26:24
0:57:36
0:28:48 0:25:14
0:00:00
Single Processor Multi Processor
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7. Multi-Processing Results (single large file vs. small tiles)
MARS® Grid Export Multi-Processing Benchmark Results
Multiprocessing with 16 processors yielding Multiprocessing with 16 processors yielding
a 79% time savings for single file export an 81% time savings for tiled export
0:23:02
0:20:10
0:17:17
0:14:24
HH:MM:SS
0:21:33
0:11:31
0:15:04
0:08:38
0:05:46
0:04:27
0:02:53 0:02:49
0:00:00
Single File Export Single Processor Single File Export Multi Processor
Tiled Export Single Processor Tiled Export Multi Processor
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8. Multi-Processing # of CPUs Performance Curve
MARS® Multi-Processing Times (Runtime per # of CPUs)
1:55:12
Processor Intensive = High
1:40:48
Processor Intensive = Medium
1:26:24
1:12:00
HH:MM:SS
0:57:36
0:43:12
0:28:48
0:14:24
0:00:00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Number of Processors
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9. General-Purpose Graphics Processing Unit (GP-GPU)
 CPUs are designed to handle a variety of different
applications and operating system needs (currently
maxed at 16 cores)
 GPUs were originally designed to handle video
rendering and screen display
 GP-GPUs can be used for massively parallel
processing with the use of SDKs like NVIDIA’s
CUDA (Compute Unified Device Architecture)
 GP-GPU processing system enhancements can be
accomplished by the use of one or more graphics
cards in a workstation or rack mounted GPU server
clusters (thousands of processor cores)
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10. CPU vs. GPU Speeds
August 24, 2009 - Nvidia’s CEO predicted that GPU computing will experience a rapid performance boost over the next six
years. According to Jen-Hsun Huang, GPU computing is likely to increase its current capabilities by 570x, while 'pure'
CPU performance will progress by a limited 3x. This would require tripling the speed of the GPU every year.
Source: NVIDIA webinar: http://www.nvidia.com/content/webinar/Tesla_Fermi_Webinar_Jan13_10_v1_1.pdf
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11. Internal Computer Hard Drives
 Technologies
 Solid state (no moving parts)
 Pros
 Faster start up
 Faster read/write
 Fragmentation has little effect
 Silent operations
 More reliable
 Can endure shock, high altitude, extreme temperatures
 Cons (currently)
 Lower capacities
 More expensive
 Spinning (most widely used)
 Typical types
 SAS (Serial Attached SCSI) – successor to SCSI
 SATA (Serial AT Attachment) – successor to ATA
 Performance
 SAS – faster 15,000 RPMs
 SATA – slower 9,200 RPMs
 Size
 SAS – up to 500 GB
 SATA – up to 2 TB
 RAID levels
 10 (RAID 1+0) – highest performance
 50 (RAID 5+0) - larger space, more redundancy
 Connector types
 iSCSI – quick install, less hardware
 Fiber – complex install, more hardware
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12. Writing Data to Temporary Local Drive Space
MARS® Grid Export Testing Network and Local Drive I/O
80% export time savings
writing to local drive then
moving product to
network drive
4:48:00
4:19:12
3:50:24
3:21:36
2:52:48
HH:MM:SS
4:22:59
2:24:00
1:55:12
1:26:24
0:55:27
0:57:36 0:47:47
0:28:48
0:00:00
Export to grid across network (input and output on network)
Export to grid across network (input on network, output to local drive temp space and then moved to network)
Export to grid with system drive (input and output on local drive)
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13. High Speed Local Area Networks (LAN)
Gigabit vs. 10 Gigabit Ethernet Network File Copy Times
39% disc I/O time savings
using 10 Gbps as
compared to 1 Gbps
22.66 GB of varying files sized
from 6 kb to 18 GB (files read
and written using Windows
0:03:36 Server 2008 R2)
0:02:53
HH:MM:SS
0:03:30
0:02:10
0:02:08
0:01:26
0:00:43
0:00:00
Gigabit 10 Gigabit
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14. Disc I/O Improvements in Windows Operating Systems
File Copy Times For Windows Operating System
70% disc I/O time savings
using Windows 7 as
compared to Windows XP
0:05:46
4.66 GB of varying files
sized from 6 kb to 1.2 GB
0:05:02
0:04:19
0:03:36
HH:MM:SS
0:05:17
0:02:53
0:02:10
0:01:26 0:01:56
0:01:36
0:00:43
0:00:00
Windows XP (Windows 2003) Windows Vista (Windows 2008) Windows 7 (Windows 2008 R2)
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15. Server-side Processing
 Network – 10GE  File server(s)
 Processing server(s)  Scalable
 Fast processor  Tiered storage for best
technology – Nehalem performance
microarchitecture
 SSD – for temporary,
 Multi-processor CPUs, unfragmented files
8 or more cores  SAS – for fast
processing
 Fast local HDD (does
not have to be huge)  SATA – for large
storage
 GP-GPU cluster
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16. Clustering
 Distributed Processing
 Typically designed to work within a LAN environment
 Highly scalable
 Scheduled processes
 Harvest free CPU clock cycles
 Very configurable
 Resource limits
 Priorities
 Time limits
 Cloud Computing
 Shared computer processing resources via the Internet by
renting usage from a third-party provider
 Data and software is usually stored on remote servers
 Key features
 Agility  Scalability
 Cost  Security
 Device and location  Maintenance
independence
 Metering
 Multi-tenancy
 High performance
 Reliability
Source: Wikipedia http://en.wikipedia.org/wiki/Cloud_computing
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17. Summary
Technologies Recommendation
Processor microarchitecture Nehalem
32/64 bit 64-bit*
Multi-processing Multi-Core / Multi-Thread*
Number of CPUs 8 or more*
Amount of RAM 8GB or more*
GP-GPU processing*
Beyond CPU processing
(higher end Nvidia card is worth the money)
SAS with iSCSI connection
Internal Hard Drives
(SSD when price drops and size increases)
Read/Write “trick” File server → local internal HDD → file server*
LAN 10 Gigabit Ethernet (10GE)
Operating System (if using Windows) Windows 7 / Server 2008 R2*
Processing architecture Server-side
Clustering Distributed processing or Cloud computing*
* If software supports this functionality
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18. Thank you!
Any questions?
Matthew Bethel, GISP
Manager of Systems Engineering
Email: matt.bethel@merrick.com
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