We Make Compute Faster. We Slash Data Center Energy Usage.

TALP (Time Affected Linear Processes)-based parallelization optimizes code at the atomic level for both the relevant architecture and data. It uses a proprietary form of functional decomposition to derive predictive analytics that determine the precise configuration necessary for code to run substantially faster at the machine level. Notably, the use of TALPs obviates the need for time-consuming loops to handle conditional statements, thereby slashing the space-time complexity and associated overhead of code. The result is code that runs much faster without the need for additional power, causing a substantial reduction in the power consumed per job.