Abstract: Methods and systems are provided for auto-optimization of gamification mechanics. A gamification platform, hosted on any suitable interface, may collect action data from one or more employee. Gamification mechanics are applied to the actions to compute an effectiveness value for each action. An optimization layer of the system may receive performance metrics for the employee from internal systems, or via third party systems. The performance metrics may be employed to update the gamification mechanics by optimizing weights for each action in a fitness function. Effectiveness predictions for future actions are generated using these optimized weights. The updated mechanics and predictions may be provided back to the gamification platform to iteratively repeat the process.

Abstract: An improved method for dividing and distributing the work of an arbitrary algorithm, having a predetermined stopping condition, for processing by multiple computer systems. A scheduler computer system accesses a representation of a plurality of work units, structured as a directed graph of dependent tasks, then transforms that graph into a weighted graph in which the weights indicate a preferred path or order of traversal of the graph, in turn indicating a preferred order for work units to be executed to reduce the impact of inter-work unit dependencies. The scheduler computer system then assigns work units to one or more worker computer systems, taking into account the preferred order.

Abstract: A base computer system obtains a set of definitions of calculations to be performed, and periodically monitors a data store to see if the data required for the calculations are available. When the required data for a given calculation are available, the base computer system sends the data and calculation instructions to a group of one or more remote computer systems for execution. The remote computer systems may be equipped with Graphics Processing Units (GPUs) for high-performance computation. The base computer system then awaits the return of reports from the one or more remote computer systems.

Abstract: This invention relates to a machine implemented method of executing CPU instructions on a plurality of computers in one or more locations, logically arranged in a weighted, lattice-like structure representing information about CPUs, CPU cores, operating system threads, network interconnects, and computer locations in a many-to-many relationship. This approach, by weighting nodes and costing edges, provides a natural method for commoditizing the execution of a workload. Furthermore, this approach lends itself to a means of determining the incremental value (or cost) of additional nodes. Consequently, the creation of a virtual crowd-sourcing market—in which either CPUs singularly or lattices as a whole are market participants—is a natural extension of the method.