1. SequenceL is a programming language that allows users to easily write parallel code that utilizes all CPU cores and GPUs for improved performance. 2. Several customers were able to achieve significant speedups over existing solutions by rewriting algorithms and applications in SequenceL, including over 2x, 17.8x, and 26x speedups in computational fluid dynamics simulations. 3. SequenceL also offers advantages in code readability, portability across platforms, and faster development times compared to alternative solutions like C, C++, OpenMP, and Matlab.

1. texasmulticore.com
Sample Customer Use Cases & Results
February 2017
Customer quote:
“SequenceL is Matlab on Steroids”

2. Customer Example: Predict Financial Market Using Neural Network
Users Learn SequenceL and Implement Significant AI Application in <2 weeks
 Challenge:
─ Predict financial market with retrospective data using
artificial neural network (AI) algorithms
─ Speed is key for big data
─ Goal: Limit time for analysis to 1 minute
 Solution
─ Implement neural network algorithms in SequenceL to
utilize full potential of user’s computer
 Results
─ Customer went from zero-to-proficient in <2 weeks,
with no outside assistance needed
─ Downloaded SequenceL, learned it via online tutorial,
and wrote significant application
─ Complex algorithms moved to SequenceL easily
─ Faster analysis of the data for end users
─ More effective use of hardware; uses all CPU cores
─ Scalability/portability on any hardware platform
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Retrospective financial data from 08/18/2006 to 11/29/2016
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All Rights Reserved
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3. Customer Example: Seismic RTM for Oil & Gas
Achieves >2x Speedup on Existing Platform with <50% Code + Portability
 Challenge:
─ ION Geophysical has one of the best and most optimized RTM codes in the oil & gas industry
─ ION RTM C/C++ and MPI was given to Acceleware to optimize with CUDA for GPUs and it ran slower
─ Goal: “Our customers don’t pay us to make the code run faster, they pay us for the geoscience. Our team
should be focused on the geoscience, not low-level SIMD instructions.”
─ Goal: Achieve platform portability + optimization to run our RTM on other platforms
 Solution
─ TMT refactored computationally-intensive
portions of existing C/C++ and MPI code
into SequenceL
 ~10,000 lines of 40,000 LOC program
 Results
─ >2x speedup on existing platforms
─ Platform dependencies removed
─ 5x speedup on POWER8 platform
─ <50% as many lines of code, much more
readable and supportable
3
© 2017 Texas Multicore Technologies, Inc.
All Rights Reserved
“Matlab on steroids”

4. Customer Example: CFD (Computational Fluid Dynamics)
SwRI Achieves 17.8x 26x Speedup with 25% Less Code
 Challenge:
─ Researchers already often have to wait weeks for these fluid flow simulation runs
─ This slows the pace of innovation and makes it impractical to further enhance models for higher
accuracy (e.g.- irregular shaped particles)
─ Goal: reduce the runtime of in-house Lattice Boltzmann method-based CFD simulation by a factor of 10
 Solution
─ SwRI reformulated this existing Fortran+OpenMP code into SequenceL
 Results
─ “SequenceL implementation was 26x faster for most relevant benchmark“ (70 particles)
─ “Runs that previously took 2 weeks now completed overnight”
─ “The SequenceL compiler created a parallel executable with no burden on the programmer and is
provably race-free”
─ “SequenceL version was 25% shorter (LOC) and closely resembles the mathematical equations”
4
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All Rights Reserved
2 weeks
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Dramatically reduced time to discovery; now practical to enhance model12 hours!

5. Customer Example: Industrial Control Networking
(WirelessHART, IEC 62591, IEEE 802.15.4)
 Challenge
─ WirelessHART was too complex and slow to process at scale
 Generating Downlink graphs for 200 nodes would take 5 hours and 20 minutes
 Generating Uplink, Downlink, and Broadcast network graphs for a plant
or oil field with a 1,000 nodes would take a solid month to process
─ Goal: Generate graphs for a 200 node network in <1 minute
 Solution
─ Emerson Research proposed a new Downlink algorithm (SRDR) in a whitepaper
─ Had TMT implement in SequenceL to achieve multicore acceleration
 Results
─ SequenceL finished by TMT in 3 weeks, ran 10x faster
 10X faster performance and right the first time
 Generated graphs in 9.3 seconds
─ Inventors took 5 months in Java, never got performance
 Initially had errors and 10x slower
 Used SequenceL to debug Java, get perf. insights (still 3x slower)
5
Fast & robust code, faster time to market
© 2017 Texas Multicore Technologies, Inc.
All Rights Reserved

6. Customer Example: Video Processing Using SequenceL
 Proprietary algorithms remove air turbulence, radiated heat, etc.
 Goal: 30Hz (fps) to keep up with real time input video feed
 Best performance (8 core x86 platform)
─ 58 Hz: SequenceL
─ 21 Hz: Matlab (Interpreter)
─ 1.2 Hz: Matlab (Coder/C-out)
Input video feed Processed video output
© 2017 Texas Multicore Technologies, Inc.
All Rights Reserved6

7. Customer Example: Video Processing Using SequenceL
 Proprietary algorithms remove air turbulence, radiated heat, etc.
 Goal: 30Hz (fps) to keep up with real time input video feed
 Best performance (8 core x86 platform)
─ 58 Hz: SequenceL
─ 21 Hz: Matlab (Interpreter)
─ 1.2 Hz: Matlab (Coder/C-out)
Input video feed Processed video output
© 2017 Texas Multicore Technologies, Inc.
All Rights Reserved7

8. Customer Example: Video Processing Using SequenceL
 Proprietary algorithms remove air turbulence, radiated heat, etc.
 Goal: 30Hz (fps) to keep up with real time input video feed
 Best performance (8 core x86 platform)
─ 58 Hz: SequenceL
─ 21 Hz: Matlab (Interpreter)
─ 1.2 Hz: Matlab (Coder/C-out)
Input video feed Processed video output
© 2017 Texas Multicore Technologies, Inc.
All Rights Reserved8

9. Customer Example: Video Processing Using SequenceL
 Proprietary algorithms remove air turbulence, radiated heat, etc.
 Goal: 30Hz (fps) to keep up with real time input video feed
 Best performance (8 core x86 platform)
─ 58 Hz: SequenceL
─ 21 Hz: Matlab (Interpreter)
─ 1.2 Hz: Matlab (Coder/C-out)
Input video feed Processed video output
© 2017 Texas Multicore Technologies, Inc.
All Rights Reserved9
SequenceL also won on code readability

10. SequenceL Changes the Game
© 2017 Texas Multicore Technologies, Inc.
All Rights Reserved10
Faster Performance
Uses all cores, GPUs
10X Faster Time to Market
Prototyping tool = Production tool
Get it Right the First Time
Far fewer lines of code; Algorithms in
SequenceL often match their definition
Portability + Optimization
True application portability with
high performance
Built on Open Industry Standards
Integrates with existing tools,
languages, & methodologies
Fast ROI
No need for expensive “parallel ninjas”
nor the time they add to the schedule
Accelerates time to innovation/discovery

11. SequenceL Changes the Game
© 2017 Texas Multicore Technologies, Inc.
All Rights Reserved11
Faster Performance
Uses all cores, GPUs
10X Faster Time to Market
Prototyping tool = Production tool
Get it Right the First Time
Far fewer lines of code; Algorithms in
SequenceL often match their definition
Portability + Optimization
True application portability with
high performance
Built on Open Industry Standards
Integrates with existing tools,
languages, & methodologies
Fast ROI
No need for expensive “parallel ninjas”
nor the time they add to the schedule
Business Agility: TTM, new platforms, Cloud