Architecture
Kinetica is an analytics database designed from the ground up to leverage parallel compute capabilities of GPUs and modern 'vectorized' CPUs. This introduces a new level of brute-force compute power that breaks open the door to faster and more flexible querying across large and streaming datasets.
The secret sauce behind breakthrough analytics lies in Kinetica's ability to utilize modern vectorized CPUs and GPUs
The CPU has been the core of the computer for decades. Database systems have evolved alongside using sequential processing to perform calculations.
Take this example of an array of numbers. To add five to each number and place them into a new array, a CPU will rapidly work through the list.
But this sequential process has its limits.
GPUs which typically have thousands of cores were designed to speed up drawing of graphics on a screen. Instead of rendering a pixel at a time, a GPU could render a whole screen in one go – a technique known as single-instruction, multiple data (SIMD).
It turns out this same capability is well suited to performing repeated similar instructions on data in parallel. With Intel's Advanced Vector Extensions (AVX) making it into CPUs in the data center, the path is now wide open to leverage vectorized compute in the cloud for analytics workloads.
Kinetica was designed from the ground up to leverage the vectorization capabilities of GPUs and modern CPUs. Analytical functions in Kinetica have all been written from scratch to take advantage of vectorization.
Vectorization unleashes significant performance improvements – particularly on spatial and temporal queries at scale. Aggregations, predicate joins, windowing functions, graph solvers all operate far more efficiently.
With so much raw compute power, you won't need to worry about indexing, partitioning or downsampling.
Brute force vectorized compute means there is less need to think through schemas before data can be explored.
Simpler data structures means less to index. Combined with Kinetica's lockless, distributed architecture, data is available for query immediately after it lands.
With less to index, the database scales in proportion to the size of the data. This leads to a smaller and more predictable scale-out footprint.
Spend less time engineering schemas, and more time using your data. Business analysts have more flexibility and freedom for ad-hoc data discovery projects.
Kinetica Cloud is free for projects up to 10GB
Faster
Faster
Faster
Every five to 10 years, an engineering breakthrough emerges that disrupts database software for the better. Vectorization is the newest breakthrough gaining momentum towards widespread adoption. Early adopters are using fully vectorized databases to foster new applications and reap lower costs.
Learn more about vectorization in this white paper.
Lower TCO
With so much raw compute power, you won't need to worry about indexing, partitioning or downsampling.
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