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The World's First

Analytics Processing Unit (APU)​

Eliminate I/O and compute bottlenecks.  Decompress, decode, and process over a billon table rows per second.

DELIVER ASSEMBLY-LINE THROUGHPUT TO DATA-INTENSIVE WORKLOADS WITH ZERO CODE CHANGE

The Speedata APU optimizes the flow of data through a configurable pipeline of compute elements. These elements are reconfigured for the incoming workload, and then the data records flow through them millions of times without having to fetch and decode instructions in every cycle — a capacity bottleneck that typically makes CPUs and GPUs unfit for analytics.

ACCELERATE I/O ACROSS COLUMNAR FILE FORMATS

The APU uniquely decompresses, decodes, and processes millions (or even billions) of records from Parquet or ORC files per second, eliminating the bottlenecks created by other chips that have to write and store intermediate data back to memory. 

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ACCELERATE QUERIES FOR A HUGE VARIETY OF DATA

While CPUs and GPUs don't handle branch divergence or variable field lengths well, the APU is designed to execute a wide variety of tasks in parallel and handle any data type and field length found in the database.

SEAMLESSLY INTEGRATE INTO EXISTING WORKLOADS 

The APU automatically intercepts the work that was previously going to the CPU and reroutes it to accelerate hardware performance with zero code change and minimal overhead. This unburdens data engineers from having to migrate their workloads and manage testing and debugging, just for modest speedups from processors not designed for analytics.

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WHY SPEEDATA APU?

Boost top line

Accelerate 100x faster time to insight

Easily answer previously impossible questions

Pre-process data to unlock value from AI workloads

Lower costs

Process more data with a fraction of the servers

Cut data center footprint and energy costs up to 95%

Eliminate opportunity cost of biz-critical projects 

Increase productivity

Turn long-running jobs into interactive workflows

Rapidly test new ideas, expand, and refresh data

Unburden infra and IT teams from hardware maintenance

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