Abstract: An analysis system may perform network analysis on data gathered from an executing application. The analysis system may identify relationships between code elements and use tracer data to quantify and classify various code elements. In some cases, the analysis system may operate with only data gathered while tracing an application, while other cases may combine static analysis data with tracing data. The network analysis may identify groups of related code elements through cluster analysis, as well as identify bottlenecks from one to many and many to one relationships. The analysis system may generate visualizations showing the interconnections or relationships within the executing code, along with highlighted elements that may be limiting performance.

Abstract: An analysis system may perform network analysis on data gathered from an executing application. The analysis system may identify relationships between code elements and use tracer data to quantify and classify various code elements. In some cases, the analysis system may operate with only data gathered while tracing an application, while other cases may combine static analysis data with tracing data. The network analysis may identify groups of related code elements through cluster analysis, as well as identify bottlenecks from one to many and many to one relationships. The analysis system may generate visualizations showing the interconnections or relationships within the executing code, along with highlighted elements that may be limiting performance.

Abstract: An analysis system may perform network analysis on data gathered from an executing application. The analysis system may identify relationships between code elements and use tracer data to quantify and classify various code elements. In some cases, the analysis system may operate with only data gathered while tracing an application, while other cases may combine static analysis data with tracing data. The network analysis may identify groups of related code elements through cluster analysis, as well as identify bottlenecks from one to many and many to one relationships. The analysis system may generate visualizations showing the interconnections or relationships within the executing code, along with highlighted elements that may be limiting performance.

Abstract: An analysis system may perform network analysis on data gathered from an executing application. The analysis system may identify relationships between code elements and use tracer data to quantify and classify various code elements. In some cases, the analysis system may operate with only data gathered while tracing an application, while other cases may combine static analysis data with tracing data. The network analysis may identify groups of related code elements through cluster analysis, as well as identify bottlenecks from one to many and many to one relationships. The analysis system may generate visualizations showing the interconnections or relationships within the executing code, along with highlighted elements that may be limiting performance.

Abstract: An offline optimization for computer software may involve creating optimized parameters or components for a software product, and charging customers for the optimization service. The software product may be distributed under one licensing regime and the optimization components may be distributed under a second licensing regime. In some embodiments, a low cost or no-cost monitoring system may be provided, which may interface with a remote service that optimizes the software product for its current workload. A user may pay for the remote optimization service through a subscription, pay-per-use, pay-for-performance, or other payment models.

Abstract: A set of optimizations may be defined in a configuration database. The configuration database may be defined with a set of boundaries that may define conditions under which the optimizations may be valid. When the conditions are not met, a new configuration database may be requested from an optimization server. The system may be used to distribute and manage optimizations for an application, which may be deployed in interpreted or runtime scenarios or in pre-execution or compiled scenarios.

Abstract: Memoizable functions may be identified by analyzing a function's side effects. The side effects may be evaluated using a white list, black list, or other definition. The side effects may also be classified into conditions which may or may not permit memoization. Side effects that may have de minimus or trivial effects may be ignored in some cases where the accuracy of a function may not be significantly affected when the function may be memoized.

Abstract: Memoization may be deployed using a configuration file or database that identifies functions to memorize, and in some cases, includes input and result values for those functions. At compile time, functions defined in the configuration file may be captured and memoized. During compilation or other pre-execution analysis, the executable code may be modified or otherwise decorated to include memoization code. The memoization code may store results from a function during the first execution, then merely look up the results when the function may be called again. The memoized value may be stored in the configuration file or in another data store. In some embodiments, the modified executable code may operate in conjunction with an execution environment, where the execution environment may optionally perform the memoization.

Abstract: Memoization may be deployed using a configuration file or database that identifies functions to memorize, and in some cases, includes input and result values for those functions. As an application is executed, functions defined in the configuration file may be captured and memoized. During the first execution of the function, the return value may be captured and stored in the configuration file. For subsequent executions of the function, the return value may be stored in the configuration file. In some cases, the configuration file may be distributed with the return values to client computers. The configuration file may be created by one device and deployed to other devices in some deployments.

Abstract: A computer software execution system may have a configurable memory allocation and management system. A configuration file or other definition may be created by analyzing a running application and determining an optimized set of settings for the application on the fly. The settings may include memory allocated to individual processes, memory allocation and deallocation schemes, garbage collection policies, and other settings. The optimization analysis may be performed offline from the execution system. The execution environment may capture processes during creation, then allocate memory and configure memory management settings for each individual process.

Abstract: A function may be memoized when a side effect is a read only side effect. Provided that the read only side effect does not mutate a memory object, the side effect may be considered as an input to a function for purity and memoization analysis. When a read only side effect may be encountered during memoization analysis, the read only side effect may be treated as an input to a function for memoization analysis. In some cases, such side effects may enable an impure function to behave as a pure function for the purposes of memoization.

Abstract: A configurable memory allocation and management system may generate a configuration file with memory settings that may be deployed at runtime. An execution environment may capture a memory allocation boundary, look up the boundary in a configuration file, and apply the settings when the settings are available. When the settings are not available, a default set of settings may be used. The execution environment may deploy the optimized settings without modifying the executing code.

Abstract: A function's purity may be estimated by comparing a new input vector to previously analyzed input vectors. When a new input vector is within a confidence boundary, the new input vector may be treated as a known vector, even when that vector has not been evaluated. The input vector may reflect the input parameters passed to a function, and the function may be analyzed to determine whether to memoize with the input vector. The function may be a function that behaves as a pure function in some circumstances and with some input vectors, but not with others. By memoizing the function when possible, the function may be executed much faster, thereby improving performance.

Abstract: A function may be selected for memoization when the function indicates that memoization may result in a performance improvement. Impure functions may be identified and ranked based on operational data, which may include length of execution. A function may be selected from a ranked list and analyzed for memoization. The memoization analysis may include side effect analysis and consistency analysis. In some cases, the optimization process may perform optimization on one function at a time so as to not overburden a running system.

Abstract: Memoization may be deployed using a configuration file or database that identifies functions to memorize, and in some cases, includes input and result values for those functions. The configuration file or database may be created by profiling target code and offline or otherwise separate analysis of the profiling results. The configuration file may be used by an execution environment to identify which functions to memorize during execution. The offline or separate analysis of the profiling results may enable more sophisticated analysis than could otherwise be performed in parallel with executing the target code, including historical analysis of multiple instances of the target code and sophisticated cost/benefit analysis.

Abstract: A configurable memory allocation and management system may generate a configuration file with memory settings that may be deployed prior to runtime. A compiler or other pre-execution system may detect a memory allocation boundary and decorate the code. During execution, the decorated code may be used to look up memory allocation and management settings from a database or to deploy optimized settings that may be embedded in the decorations.

Abstract: The purity of a function may be determined after examining the performance history of a function and analyzing the conditions under which the function behaves as pure. In some cases, a function may be classified as pure when any side effects are de minimis or are otherwise considered trivial. A control flow graph may also be traversed to identify conditions in which a side effect may occur as well as to classify the side effects as trivial or non-trivial. The function purity may be used to identify functions for memoization. In some embodiments, the purity analysis may be performed by a remote server and communicated to a client device, where the client device may memoize the function.