TMT SequenceL customer use cases and results

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Sample Customer Use Cases & Results

February 2017

Customer quote:

“SequenceL is Matlab on Steroids”

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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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

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All Rights Reserved

“Matlab on steroids”

https://en.wikipedia.org/wiki/ION_Geophysical

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”

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All Rights Reserved

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Dramatically reduced time to discovery; now practical to enhance model 12 hours!

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)

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Fast & robust code, faster time to market


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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


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SequenceL also won on code readability

SequenceL Changes the Game



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

SequenceL Changes the Game



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

Software

TMT SequenceL customer use cases and results