Sqream DB on OpenPOWER performance

Sqream DB on OpenPOWER performance

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  CONQUERING THE CHALLENGES GROWINGDATA STORES OF ANALYZING
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  WHAT WE AREHEARING  We have a lot of data, but analyze only a fraction ~ 10-20%  New system and new use cases generate data at rates of TB per day  Enhancing the insights requires larger and more diverse data  Current Big Data analytics queries take longer and longer  Existing systems:  Are limited for current needs and will not scale  Are costly and complicated  Require extensive support and maintenance staff
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  2008 <1-4TB 2010 <10TB 2016 TB-PB ENTERPRISE DATA STORESARE GROWING EXPONENTIALLY Technology CPU Technology GPU
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  BUT DATA STORESWERE NOT BUILT TO HANDLE THIS LEVEL OF DATA NoSQL & Hadoop GPU Database Relational DB 1970s-1990s 1990-2010 MPP 2005-2010 In-Memory / Performance 2010… Massive Data Kinetica Aerospike Mongo DB SQREAM DB MapD MemSQL VoltDB DB2 BLU IBM Netezza IBM Oracle DB2 Teradata Vertica Redshift Exadata Oracle Server SQL Classic Relational Hadoop Hadoop+ Hadoop+++
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  VALUABLE INSIGHTS GO UNDISCOVERED BILost 90%Data Analyzed <10%
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  BUSINESS VALUE GENERATORS •Ensure short time to analysis • Maximize amount and variety of data to be analyzed • Fast ongoing performance (ingest and consume)
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  NEW PARADIGM REQUIRED TOHANDLE MASSIVE DATA Keep it simple • Use the most common interface - ANSI SQL • Remove the software complexity layers • Eliminate the extra efforts of indexes, projections, partitions etc… • A simple solution to acquire, install and maintain… how about a 2U server or a few of them Specific Architecture • Disconnect the compute from the storage and augment the CPU with a much faster growing compute resource • Create a unique data structure, specifically designed for very large amounts of data (hint – compressed) • Design your technology to optimize HW usage
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  SQREAM DB • Massivelyparallel engine • Faster and smaller than CPUs POWERED BY GPUs • Terabytes to petabytes • Not limited by RAM • Ingests 3 TB/hr/GPU • Powerful columnar storage • Always-on compression • Familiar ANSI SQL • Standard connectors • 100 TB in a 2U server • Highly cost-efficient • Python, AI, Jupyter, etc. • Built for data science COMPLEMENTS YOUR EXISTING DATA STORES MASSIVELY SCALABLE SQL DATABASE EXTENSIBLE FOR ML/AI MINIMAL FOOTPRINT LIGHTNING FAST
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  GPU-ACCELERATED DATA WAREHOUSE SQREAMDB BOOSTS QUERY PERFORMANCE BY UP TO 150% FOR IBM POWER9 USERS “GPU-accelerated analytics are an increasingly important part of our industry,” said Sumit Gupta, VP of HPC and AI for IBM Cognitive Systems. “The announcement of SQream on the IBM POWER9 platform takes this concept to another level of performance, as the POWER9 CPU with embedded NVIDIA NVLink interface to NVIDIA’s GPUs allows SQream to enable even faster processing of data on POWER9 servers.”
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  HIGH THROUGHPUT CONVERGED •SQream DB designed for high-throughput • IBM Power Systems is the only NVLink CPU-to-GPU enabled architecture • IBM AC922, with POWER9 and NVLINK can transfer data at up to 300GB/s, almost 9.5x faster than PCIe 3.0 found in x86- based architectures, reducing classic I/O bottlenecks 2x NVIDIA Tesla V100 2x NVIDIA Tesla V100 IBM Power 9 IBM Power 9
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  HIGH THROUGHPUT ARCHITECTURE IT’SNOT JUST THE CORES RAM Power9 CPU Tesla V100 GPU VRAM Tesla V100 GPU VRAM 170GB/s per CPU NVLink – 300GB/s BiDi 900GB/s RAM Power9 CPU Tesla V100 GPU VRAM Tesla V100 GPU VRAM IBM SMP bus
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  UP TO 2xFASTER LOADING SQREAM DB ON POWER9 • SQream DB relies on CPU as well as GPUs for loading • IBM’s Power9 multi-core architecture makes loading much faster than comparable x86 based systems • IBM Power9 system loaded data nearly twice as fast as the x86 based machine IBM Power9 AC922: 2x POWER9 16C @ 3.8GHz | 256 GB DDR4 2666 MHz | SSD storage | 4x NVIDIA Tesla V100 (SXM2 NVLINK - 16GB) Dell PowerEdge R740: 2x Intel Xeon Silver 4112 CPU @ 2.60GHz | 256GB DDR4 2666MHz | SSD storage | 4x NVIDIA Tesla V100 (PCIe - 16GB) Load Time (seconds) LoadTime(seconds) Lowerisbetter Load time for 6 billion TPC-H records Dell Poweredge R740 IBM Power9 AC922
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  UP TO 3.7xFASTER QUERIES SQREAM DB ON POWER9 • SQream DB on Power9 is between 150% to 370% faster than comparable x86 architectures • The CPU-GPU NVLink bandwidth is key to performance in complex queries IBM Power9 AC922: 2x POWER9 16C @ 3.8GHz | 256 GB DDR4 2666 MHz | SSD storage | 4x NVIDIA Tesla V100 (SXM2 NVLINK - 16GB) Dell PowerEdge R740: 2x Intel Xeon Silver 4112 CPU @ 2.60GHz | 256GB DDR4 2666MHz | SSD storage | 4x NVIDIA Tesla V100 (PCIe - 16GB) 52.83 10.35 84.5 78.57 14.06 2.8 30.29 29.01 0 10 20 30 40 50 60 70 80 90 TPC-H Query 8 TPC-H Query 6 TPC-H Query 19 TPC-H Query 17 Querytime(seconds) Lowerisbetter Query SQream DB performance IBM Power9 vs Intel Xeon (Skylake) Dell PowerEdge R740 IBM Power9 AC922
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  OUR PARTNERS Cloud Infrastructure Storageand Network Solution Visualization
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  HQ in 7WTC New York | R&D in Tel Aviv CORPORATE PROFILE FOUNDED IN 2010 with Alibaba Cloud Strategic Partnership Patents 10 Employees 60+
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  FEEL FREE TO ADDRESS Headquarters,7 WTC 250 Greenwich Street New York, New York Hanan Cohen, Account Executive hananc@sqream.com | www.sqream.com WE ARE SOCIAL CONTACT