University of Rochester

Microsoft Technology Licensing, LLC

International Business Machines Corporation

Chen Ding

Raul Esteban Silvera

Yaoqing Gao

Trishul A Chilimbi

Roch Georges Archambault

Shimin Cui

Benjamin Livshits

Emre Mehmet Kiciman

Chengliang Zhang

Pengcheng Li

Xipeng Shen

Ruke Huang

Xiaoming Gu

Yutao Zhong

Hao Luo

A liveness-based memory allocation module operating so that a program thread invoking the memory allocation module is provided with an allocation of memory including a reserve of free heap slots beyond the immediate requirements of the invoking thread. The module receives a parameter representing a thread execution window from an invoking thread; calculates a liveness metric based upon the parameter; calculates a reserve of memory to be passed to the invoking thread based upon the parameter; returns a block of memory corresponding to the calculated reserve of memory. Equations, algorithms, and sampling strategies for calculating liveness metrics are disclosed, as well as a method for adaptive control of the module to achieve a balance between memory efficiency and potential contention as specified by a single control parameter.

Parallel memory allocator employing liveness metrics

A method to quantify a plurality of digital data sharing in a multi-threaded execution includes the steps of: providing at least one processor; providing a computer readable non-transitory storage medium including a computer readable multi-threaded executable code and a computer readable executable code to calculate a plurality of shared footprint values and an average shared footprint value; running the multi-threaded executable code on the at least one computer processor; running the computer readable executable code configured to calculate a plurality of shared footprint values and an average shared footprint value; calculating a plurality of shared footprint values by use of a linear-time process for a corresponding plurality of executable windows in time; and calculating and saving an average shared footprint value based on the plurality of shared footprint values to quantify by a metric the data sharing by the multi-threaded execution. A system to perform the method is also described.

System and method to quantify digital data sharing in a multi-threaded execution

A 'code splitting tool' provides various techniques for automatically analyzing and rewriting existing browser-based applications to introduce dynamic code loading into those applications thereby improving perceived application responsiveness. Structural elements of application code (including functions, classes, etc.) are broken into small 'stubs' with corresponding bodies. Rewritten applications then initially transfer only the portion of the code (including some combination of stubs and bodies) to the client as necessary for initial application execution. Additional stubs and/or bodies are then transferred either on-demand at runtime or in the background. Automated code rewriting proceeds without requiring any application-specific knowledge or changes to existing code prior to code rewriting. Further, the code splitting tool can tailor code rewriting to specific computing devices (computers, PDA's, cell phones, etc.), specific network conditions, and/or specific users, through an automated training process that creates clusters that control code downloads to optimize perceived application responsiveness.

Automated code splitting and pre-fetching for improving responsiveness of browser-based applications

Described is predicting cache locality in a multicore/multithreaded processing environment including when threads share cache data in a non-uniform interleaving manner. Thread execution traces are analyzed to compute a set of per-thread parameters that can then be used to predict cache miss rates for other cache sizes. In one aspect, a model is based upon a probability that the cache reuse distance will increase because of accesses by other threads, and another probability that the reuse distance will decrease because of intercept accesses by other threads to shared data blocks. Estimates of the number of shared data blocks, possibly shared data blocks and private data blocks are used in the computations.

Scalability analysis for server systems

A method of dynamic parallelization for programs in systems having at least two processors includes examining computer code of a program to be performed by the system, determining a largest possible parallel region in the computer code, classifying data to be used by the program based on a usage pattern and initiating multiple, concurrent processes to perform the program. The multiple, concurrent processes ensure a baseline performance that is at least as efficient as a sequential performance of the computer code.

Parallel programming using possible parallel regions and its language profiling compiler, run-time system and debugging support

Computer implemented method, system and computer usable program code for cache management. A cache is provided, wherein the cache is viewed as a sorted array of data elements, wherein a top position of the array is a most recently used position of the array and a bottom position of the array is a least recently used position of the array. A memory access sequence is provided, and a training operation is performed with respect to a memory access of the memory access sequence to determine a type of memory access operation to be performed with respect to the memory access. Responsive to a result of the training operation, a cache replacement operation is performed using the determined memory access operation with respect to the memory access.

Optimal cache replacement scheme using a training operation

Reuse distance is the number of data which are accessed between accesses of a datum. The computation of reuse distance uses a search tree and is carried out through approximate analysis, pattern recognition, or distance-based sampling. The reuse distance can be used to detect reference affinity, that is, to detect which data are accessed together.

Growing community of inventors

Rochester, NY, United States of America

Chen Ding