Abstract: A quality-directed adaptive analytic retraining is provided. Training example data with which to retrain a machine learning model that has been previously trained is received. The training example data is stored in a memory. The machine learning model is evaluated at least by running the machine learning model with the training example data. A normalized quality measure may be determined based on the evaluating. Whether to retrain the machine learning model is determined at least based on the normalized quality measure. Responsive to determining that the machine learning model is to be retrained, the machine learning model is retrained.

Abstract: A quality-directed adaptive analytic retraining is provided. Training example data with which to retrain a machine learning model that has been previously trained is received. The training example data is stored in a memory. The machine learning model is evaluated at least by running the machine learning model with the training example data. A normalized quality measure may be determined based on the evaluating. Whether to retrain the machine learning model is determined at least based on the normalized quality measure. Responsive to determining that the machine learning model is to be retrained, the machine learning model is retrained.

Abstract: A computer readable storage medium for associating a phase with an activation of a computer program that supports garbage collection include: a plurality of stacks, each stack including at least one stack frame that includes an activation count; and a processor with logic for performing steps of: zeroing the activation count whenever the program creates a new stack frame and after garbage collection is performed; determining whether an interval has transpired during program execution; examining each stack frame's content and incrementing the activation count for each frame of the stacks once the interval has transpired; detecting the phase whose activation count is non-zero and associating the phase with the activation; and ensuring that when the phase ends, an action is immediately performed.

Abstract: Concurrent reduction optimizations for thieving schedulers may include a thieving worker thread operable take a task from a first worker thread's task dequeue, the thieving worker thread and the first worker thread having same synchronization point in a program at which the thieving worker thread and the first worker thread can resume their operations. The thieving worker thread may be further operable to create a local copy of memory locations associated with the task in local memory of the thieving worker thread, and store result of the thieving worker executing the task as the local copy. The thieving worker thread may be further operable to atomically perform a reduction operation to a master location that both the thieving worker thread and the first worker thread can access, in response to the thieving worker thread completing the task.

Abstract: A computer readable storage medium for associating a phase with an activation of a computer program that supports garbage collection include: a plurality of stacks, each stack including at least one stack frame that includes an activation count; and a processor with logic for performing steps of: zeroing the activation count whenever the program creates a new stack frame and after garbage collection is performed; determining whether an interval has transpired during program execution; examining each stack frame's content and incrementing the activation count for each frame of the stacks once the interval has transpired; detecting the phase whose activation count is non-zero and associating the phase with the activation; and ensuring that when the phase ends, an action is immediately performed.

Abstract: A system for associating a phase with an activation of a computer program that supports garbage collection include: a plurality of stacks, each stack including at least one stack frame that includes an activation count; and a processor with logic for performing steps of: zeroing the activation count whenever the program creates a new stack frame and after garbage collection is performed; determining whether an interval has transpired during program execution; examining each stack frame's content and incrementing the activation count for each frame of the stacks once the interval has transpired; detecting the phase whose activation count is non-zero and associating the phase with the activation; and ensuring that when the phase ends, an action is immediately performed. Detecting phases in a running computer program, creates an activation count associated with each stack frame.

Abstract: A system for associating a phase with an activation of a computer program that supports garbage collection include: a plurality of stacks, each stack including at least one stack frame that includes an activation count; and a processor with logic for performing steps of: zeroing the activation count whenever the program creates a new stack frame and after garbage collection is performed; determining whether an interval has transpired during program execution; examining each stack frame's content and incrementing the activation count for each frame of the stacks once the interval has transpired; detecting the phase whose activation count is non-zero and associating the phase with the activation; and ensuring that when the phase ends, an action is immediately performed. detecting phases in a running computer program, creates an activation count associated with each stack frame.

Abstract: A system and method for detecting phases in a running computer program, creates an activation count associated with each stack frame. The activation count is zeroed whenever a new frame is created in a stack and incremented for each frame encountered during periodic intervals. A phase is detected with an activation whose activation count is non-zero.

Abstract: A method for implementing an object model for an object-oriented programming language. Also contemplated is a method whereby some object state is materialized directly in those objects deemed likely to use such a state, but is externalized for those objects deemed unlikely to use the state.

Abstract: A system and method for characterizing runtime behavior of a computer program executing in an execution environment, the method comprising: identifying one or more instances of yield points in a program to be executed, each yield point indicating a potential sampling operation during program execution; during program execution, in response to an identified yield point instance, ascertaining a state of the execution environment for indicating whether a sampling operation is to be performed; and, when the state of the execution environment indicates a sampling operation, recording relevant information for characterizing behavior of the execution environment. Relevant information for characterizing program behavior includes frequencies of methods executed in the program, and calling context associated with methods called by the program.

Abstract: A sampling-based system and method for adaptively optimizing a computer program executing in an execution environment that comprises one or more compiler devices for providing various levels of program optimization. The system comprises a runtime measurements sub-system for monitoring execution of the computer program to be optimized, the monitoring including obtaining raw profile data samples and characterizing the raw profile data; a controller device for receiving the characterized raw profile data from the runtime measurements sub-system and analyzing the data for determining whether a level of program optimization for the executing program is to be performed by a compiler device, the controller generating a compilation plan in accordance with a determined level of optimization; and, a recompilation sub-system for receiving a compilation plan from the controller and invoking a compiler device for performing the level of program optimization of the executing program in accordance with the compilation plan.

Abstract: A method for characterizing runtime behavior of a computer program executing in an execution environment comprising: generating a call stack runtime data structure for tracking methods currently active in an executing program thread, an active method on the call stack is represented by a frame; determining condition for sampling an executing program to determine current program behavior; and, upon determination of a sampling condition, the sampling including examining at least one frame in the call stack in response to evaluate context of one or more methods represented in the call stack, the at least one frame in the call stack providing context relating to an executing program's calling structure.

Abstract: A method for implementing an object model for an object-oriented programming language. Also contemplated is a method whereby some object state is materialized directly in those objects deemed likely to use such a state, but is externalized for those objects deemed unlikely to use the state.