05 ttg labs

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TTG Apptimizer Software Autotuning Toolkit Pavel Ivanov, PhD Co-founder and Deputy CEO, Business Development [email protected]

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Page 1: 05 ttg labs

TTG ApptimizerSoftware Autotuning

ToolkitPavel Ivanov, PhD

Co-founder and Deputy CEO, Business Development

[email protected]

Page 2: 05 ttg labs

How to get the real power of GPGPU?

10- to 20-fold increase in software

performance

10 to 30 percent increase in software

performance

THEORY

PRACTICE

Solution: OPTIMIZE your software !

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Let your software to optimize itself !

Manually Time and

resources An endless game

Autotuning Efficiently Once and forever

Traditional approach

Our approach

Multifold performance gain. AUTOMATICALLY !

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TTG Apptimizer: as simple as 1-2-3

RESULTS● Highly optimized SW – ↑ 6% in performance● Porting – ↑ 31% in performance FOR FREE !● In general – 1,5- to 3-fold performance increase

GREAT SAVINS FOR THE CUSTOMER !

● Add several lines to your source code● Compile with TTG Apptimizer● Run

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Global markets we address● Computational software and services

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● Big Data

● Game engines and MW

$11B in 2015 / 9%

$7B (6%) est.*

$24B in 2016 / 32%

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Team● Maxim Krivov – co-founder, CEO and Head of

development. Certified NVIDIA CUDA Developer. 8 years of experience in HPC ad GPU software development

● Pavel Ivanov – co-founder and Deputy CEO (BD). 21 years in IT industry and project management

● Sergey Grizan – co-founder and leading developer. MS Certifed Professional. 7 years of experience in SW development

● Mikhail Pritula – key developer in customer projects. 8 years of experience in HPC and parallel programming. TopCoder 1611.

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We need $1,7M of investments

● To enter into partnerships with vendors and resellers

● To aggressively expand our business onto global market

● To develop new products within TTG Apptimizer family

__global__ void myCudaKernel(){/*Some computations*/}

EnumParameter<int> block(«{128,…, 1024}»);…while (eps > 10e-9){ os.StartIteration(); myCudaKernel<<<N/block, block>>>(); os.FinishIteration();}

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Backup slides

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Competitive landscapeCompetitors (indirect)

● AMD, Intel, NVIDIA – non-universal solutions

● Caps, GPUSystems, Xcelerit, StarPU,… – no optimization!

Partnership vs Competition

Our advantages

● Software optimizes itself● Universal solution● Shorter time-to-market● Less developer’s skills

Up to 300-fold performance increase*

10Traditional approach (per year)

TTG Apptimizer (once)

0 2 4 6 8 10 12 14 16

Video Streams. Optimization efforts, days

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Software optimization: our approach

GPGPU: breakthrough in computations

Porting SW to GPU (semi)automatically

SW performance boosts 10- to 20-fold (expectations)

SW performance boosts 10 to 30 percent (reality)

SW is underoptimized

Optimization for GPUs: an endless race

Our once-and-forever solution:AUTOTUNING

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Product and business model Licensing

Cash flowRelated services

GPUs Cost, USD1 19905 2530

10 313020 413030 513050 713075 9130

100 11130

TTG Apptimizer● Dec. 2012 – stable

commercial version● May 2013 – version 1.0.3● First sales● 10 contracts on services● FIIP certificate on SW

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Investment needsInvestments

● $1.9M for● TTG Apptimizer versions

for clusters, supercomputers, and clouds

● Marketing, network of partners, sales channels

● Share up to 25%● IRR = 87%● ROI in 4 years

Exit

● Sale of a share to● Investment Foundation● Intel, AMD, NVIDIA

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