Abstract: Systems and methods for building applications by automatically incorporating application performance data into the application build process are disclosed. By capturing build settings and performance data from prior applications being executed on different computing systems such as bare metal and virtualized cloud instances, a performance database may be maintained and used to predict build settings that improve application performance (e.g., on a specific computing system or computing system configuration).

Abstract: Systems and methods for building applications by automatically incorporating application performance data into the application build process are disclosed. By capturing build settings and performance data from prior applications being executed on different computing systems such as bare metal and virtualized cloud instances, a performance database may be maintained and used to predict build settings that improve application performance (e.g., on a specific computing system or computing system configuration).

Abstract: Systems and methods for allocating computing resources within a distributed computing system are disclosed. Computing resources such as CPUs, GPUs, network cards, and memory are allocated to jobs submitted to the system by a scheduler. System configuration and interconnectivity information is gathered by a mapper and used to create a graph. Resource allocation is optimized based on one or more quality of service (QoS) levels determined for the job. Job performance characterization, affinity models, computer resource power consumption, and policies may also be used to optimize the allocation of computing resources.

Abstract: A system and method for processing application performance using application phase differentiation and detection is disclosed. Phase detection may be accomplished in a number of different ways, including by using a deterministic algorithm that looks for changes in the computing resource utilization patterns (as detected in the performance data collected). Machine learning (ML) and neural networks (e.g. sparse auto encoder SAE) may also be used. Performance data is aggregated according to phase and stored in a database along with additional application and computing system information. This database may then be used to find similar applications for performance prediction.

Abstract: Systems and methods for allocating computing resources within a distributed computing system are disclosed. Computing resources such as CPUs, GPUs, network cards, and memory are allocated to jobs submitted to the system by a scheduler. System configuration and interconnectivity information is gathered by a mapper and used to create a graph. Resource allocation is optimized based on one or more quality of service (QoS) levels determined for the job. Job performance characterization, affinity models, computer resource power consumption, and policies may also be used to optimize the allocation of computing resources.

Abstract: A system and method for processing application performance using application phase differentiation and detection is disclosed. Phase detection may be accomplished in a number of different ways, including by using a deterministic algorithm that looks for changes in the computing resource utilization patterns (as detected in the performance data collected). Machine learning (ML) and neural networks (e.g. sparse auto encoder SAE) may also be used. Performance data is aggregated according to phase and stored in a database along with additional application and computing system information. This database may then be used to find similar applications for performance prediction.

Abstract: A system and method for generating performance assistance charts is disclosed. An application performance spectrometer aggregates collected application performance data by scaling, normalizing and quantizing the data so that all samples indicative of low performance appear on one side of the graph, all samples indicative of high performance appear on the other side of the graph, and samples in between are positioned relative to those two poles in quantized buckets. The spectrometer may be used to visualize an application's performance characteristics, and as an application fingerprint may be used to compare different applications and determine which have similar performance profiles.

Abstract: Systems and methods for building applications by automatically incorporating application performance data into the application build process are disclosed. By capturing build settings and performance data from prior applications being executed on different computing systems such as bare metal and virtualized cloud instances, a performance database may be maintained and used to predict build settings that improve application performance (e.g., on a specific computing system or computing system configuration).

Abstract: Systems and methods for collecting performance data in high performance computing systems are disclosed. To prevent massive amounts of performance data from overwhelming the system and negatively impacting performance, collected performance data may be processed into two databases: (i) an aggregate database, and (ii) a time-series database holding the newest information for real time performance analysis. Storage space may be saved by using a FIFO buffer to store collected performance data. A real-time performance collection engine may adjust the performance sampling interval used and the particular performance counters used based on measured system impact and feedback from other system modules consuming the performance data.

Abstract: Systems and methods for estimating the scalability of applications in high performance computing and distributed computing environments and for configuring applications based on those estimates are disclosed. A model is disclosed that provides an estimate of the scalability behavior of an application based on basic parameters and a small number of runs on bare metal and cloud systems. The system may also be configured to use the estimated performance to recommend optimal configurations based on different policies, including best performance, lowest cost, and best performance per cost.