Methods and systems for enhanced assessment and scoring of high-performance compute architectures

A computer-program product comprising a non-transitory machine-readable storage medium storing computer instructions that, when executed by one or more processors, perform operations comprising:

• at a compute architecture optimization service:
• obtaining, via a graphical user interface, a set of compute architecture design parameters associated with a target subscriber, wherein the set of compute architecture design parameters include:
• one or more distinct types of artificial intelligence (AI) compute tasks the target subscriber intends to execute within a HPC environment, and
• a set of compute architecture design constraints specifying one or more immutable boundaries for controlling a compute architecture design space used by the compute architecture optimization service;
• generating, by the one or more processors, a HPC architecture data object for the target subscriber that satisfies the set of compute architecture design parameters obtained via the graphical user interface, wherein generating the HPC architecture data object includes:
• translating the set of compute architecture design parameters into a set of hardware components and a set of software components that are (i) collectively capable of supporting execution of the one or more distinct types of AI compute tasks and (ii) reside within the compute architecture design space formed according to the set of compute architecture design constraints, and
• in response to generating the HPC architecture data object for the target subscriber:
• executing, in real-time or near real-time by the one or more processors, one or more automated pairwise assessments between the HPC architecture data object generated for the target subscriber and a reference HPC architecture data object;
• computing, in real-time or near real-time by the one or more processors, a percent of potential value that indicates a degree of performance disparity between the HPC architecture data object generated for the target subscriber and the reference HPC architecture data object based on pairwise assessment findings outputted by the one or more automated pairwise assessments;
• and
• constructing, in the real-world, an optimal HPC environment corresponding to the HPC architecture data object when the percent of potential value satisfies a predetermined minimum score threshold value.

A computer-implemented system comprising:

• one or more processors;
• a memory;
• a computer-readable medium operably coupled to the one or more processors, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the one or more processors, cause a computing device to perform operations comprising:
• at a compute architecture optimization service:
• obtaining, via a graphical user interface, a set of compute architecture design parameters associated with a target subscriber, wherein the set of compute architecture design parameters include:
• one or more distinct types of artificial intelligence (AI) compute tasks the target subscriber intends to execute within a HPC environment, and
• a set of compute architecture design constraints specifying one or more immutable boundaries for controlling a compute architecture design space used by the compute architecture optimization service;
• generating, by the one or more processors, a HPC architecture data object for the target subscriber that satisfies the set of compute architecture design parameters obtained via the graphical user interface, wherein generating the HPC architecture data object includes:
• translating the set of compute architecture design parameters into a set of hardware components and a set of software components that are (i) collectively capable of supporting execution of the one or more distinct types of AI compute tasks and (ii) reside within the compute architecture design space formed according to the set of compute architecture design constraints, and
• in response to generating the HPC architecture data object for the target subscriber:
• executing, in real-time or near real-time by the one or more processors, one or more automated pairwise assessments between the HPC architecture data object generated for the target subscriber and a reference HPC architecture data object;
• computing, in real-time or near real-time by the one or more processors, a percent of potential value for the HPC architecture data object that indicates a degree of computing performance disparity between the HPC architecture data object generated for the target subscriber and the reference HPC architecture data object based on pairwise assessment findings outputted by the one or more automated pairwise assessments;
• and
• constructing, in the physical world, an optimal HPC environment corresponding to the HPC architecture data object when the percent of potential value satisfies a predetermined minimum score threshold value.