melt iron heterogeneous computing - lspe v3
TRANSCRIPT
Agenda
• The problem – exploding Data• What is heterogeneous computing
• How does it work• Some success stories• DB as a choke point
• impact on the enterprise• Nagios
• Our experience – some data• Melt Iron – who we are.
Problem – exploding Data…2.5 Exabytes/day generated
• Google handles > 24 PB• 7 billion shares• Giga (109) …Tera …Peta …Exa
…Zetta …Yottabytes
Implications for Compute• CAPEX follows curve• OPEX follows curve
Implication• Compute capability will not match growing data.
Types of DataTransactional Data / MIS, Business Apps.• Decision making for each transaction.• Key metric: Transactions per sec.• Acceleration - Faster processors & in-memory compute.
Parallel data / Big-Data / Analytics.• Pattern analysis• Math/Statistical analysis• Key metric: data size stored & processed• Acceleration - Parallel Processor.
Interdependent data / Weather Forecasting, HPC• Scenario planning, Scientific computing• Finite element analysis
Heterogenous computing (CPU+GPU)
•CPU is sequential computing•GPU is parallel computing.
•General computation on CPU•Parallel data on GPU
•CPU has 4-8-32 cores, GP-GPUs have 1K-5K cores• Analogy: a truck vs. a Freight Train
• Truck carries small load & relatively flexible• Train carries huge load and is relatively inflexible
GPU Based Computing• GPU – Graphics processing unit. Parallel
cores, used for graphics, video, streaming media.
• GP-GPU: General purpose GPUs - used in high performance computing (HPC) for very large data sets.
• Offload data intensive processing to GPU, rest to CPU.
• Power efficient data centers, Govt. Labs, Universities, Large enterprises use GP-GPU.
• Performance improvements of 50x and more.
GP-GPU stories*BNP Paribas- 10x lower power, 16x lesser space
J.P. Morgan :Risk Computation40x performance Improvement80x lower data centre costs
Bloomberg-:Fixed Income 16hrs-2hrsBond Valuation 8x faster38x lower energy costs
AON Benfield :Insurance-Risk ManagementFrom days to minutes-can respond in intra day now
Citadel: Hedge Fund70x faster pricing
* from NVIDIA
Today: DB is a choke point…
Nagios: Architecture
DB slows as records added.5 GB limit
New Apps: Analytics, ML
Answers:• cluster, partition/shard db• modify query/apps• expensive and done post-deployment…
Our experience: GP-GPU usage
Our experience: GP-GPU usage
Pattern match on:• Zeon quad-core server – 8 GB• nVidia Quadro 2000: 192 cores, 1 GB RAM
• As data size increases, CPU slows exponentially• GPU curve is almost flat.
Melt Iron• We are about parallel-computing• Focused on the Enterprise.• Huge, huge opportunity everywhere in Enterprise Compute.• Change the course of the river Amazon – from sequential to heterogeneous compute
• Open Source• Will setup meetup on heterogeneous computing• Welcome open source contributors:
• Java/C++• C/Asm• CUDA/OpenCL
contact me: [email protected]
Melt Iron: DB applianceWeb ServerJDBC/ODBC
Java/C# Enterprise App…
Web ServerJDBC/ODBC
Java/C# Enterprise App…
Web ServerJDBC/ODBC
Java/C# Enterprise App…
Database
Melt IronDB Appliance (HA)
Web ServerJDBC/ODBC
Real-time Analytics App…
Accelerate DB bymore than 100x.
