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 tracing system may divide trace objectives across multiple instances of an application, then deploy the objectives to be traced. The results of the various objectives may be aggregated into a detailed tracing representation of the application. The trace objectives may define specific functions, processes, memory objects, events, input parameters, or other subsets of tracing data that may be collected. The objectives may be deployed on separate instances of an application that may be running on different devices. In some cases, the objectives may be deployed at different time intervals. The trace objectives may be lightweight, relatively non-intrusive tracing workloads that, when results are aggregated, may provide a holistic view of an application's performance.
Abstract:
A distributed tracing system may use independent trace objectives for which a profile model may be created. The profile model may be deployed as a monitoring agent on non-instrumented devices to evaluate the profile models. As the profile models operate with statistically significant results, the sampling frequencies may be adjusted. The profile models may be deployed as a verification mechanism for testing models created in a more highly instrumented environment, and may gather performance related results that may not have been as accurate using the instrumented environment. In some cases, the profile models may be distributed over large numbers of devices to verify models based on data collected from a single or small number of instrumented devices.
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 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:
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 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:
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:
A tracing management system may use cost analyses and performance budgets to dispatch tracing objectives to instrumented systems that may collect trace data while running an application. The tracing management system may analyze individual tracing workloads for processing, storage, and network performance costs, and select workloads to deploy based on a resource budget that may be set for a particular device. In some cases, complementary tracing objectives may be selected that maximize consumption of resources within an allocated budget. The budgets may allocate certain resources for tracing, which may be a mechanism to limit any adverse effects from tracing when running an application.
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.