Embodiments of the present invention provide a memory resource optimization method and apparatus, relate to the computer field, solve a problem that existing multi-level memory resources affect each other, and optimize an existing single partitioning mechanism. A specific solution is: obtaining performance data of each program in a working set by using a page coloring technology, obtaining a category of each program in light of a memory access frequency, selecting, according to the category of each program, a page coloring-based partitioning policy corresponding to the working set, and writing the page coloring-based partitioning policy to an operating system kernel, to complete corresponding page coloring-based partitioning processing. The present invention is used to eliminate or reduce mutual interference of processes or threads on a memory resource in light of a feature of the working set, thereby improving overall performance of a computer.

A method includes saving a control state for a processor in response to commencing a transactional processing sequence, wherein saving the control state produces a saved control state. The method also includes permitting updates to the control state for the processor while executing the transactional processing sequence. Examples of updates to the control state include key mask changes, primary region table origin changes, primary segment table origin changes, CPU tracing mode changes, and interrupt mode changes. The method also includes restoring the control state for the processor to the saved control state in response to encountering a transactional error during the transactional processing sequence. In some embodiments, saving the control state comprises saving the current control state to memory corresponding to internal registers for an unused thread or another level of virtualization. A corresponding computer system and computer program product are also disclosed herein.

Methods, apparatus, systems and articles of manufacture are disclosed facilitate read-modify-write support in a coherent victim cache with parallel data paths. An example apparatus includes a random-access memory configured to be coupled to a central processing unit via a first interface and a second interface, the random-access memory configured to obtain a read request indicating a first address to read via a snoop interface, an address encoder coupled to the random-access memory, the address encoder to, when the random-access memory indicates a hit of the read request, generate a second address corresponding to a victim cache based on the first address, and a multiplexer coupled to the victim cache to transmit a response including data obtained from the second address of the victim cache.

A caching system including a first sub-cache and a second sub-cache in parallel with the first sub-cache, wherein the second sub-cache includes: line type bits configured to store an indication that a corresponding cache line of the second sub-cache is configured to store write-miss data, and an eviction controller configured to evict a cache line of the second sub-cache storing write-miss data based on an indication that the cache line has been fully written.

Disclosed are an on-demand shared data caching method, a computer program, and a computer readable medium applicable for distributed deep learning computing. The method includes a step of dynamically building a distributed shared memory cache space, in which a distributed shared memory deployment and data file access management module is added to a deep learning framework to build the distributed shared memory cache space by a memory set of a multiple of computing nodes of a cluster computer; and a distributed deep learning computing step, in which the computing node overrides a Dataset API of the deep learning framework to execute the distributed deep learning computing. When reading a data file, if the data file exists in the distributed shared memory cache space, then it will be accessed directly, or else it will be obtained from an original specified directory location and stored in the distributed shared memory cache space.

A method is provided. The method includes: receiving a plurality of characteristic information associated with a plurality of tasks allocated to a plurality of processor cores; monitoring a task execution environment while the plurality of processor cores perform the plurality of tasks based on at least one operating condition; and allocating a plurality of cache areas of at least one cache memory to the plurality of processor cores based on the plurality of characteristic information and the task execution environment. Sizes of the plurality of cache areas are set differently for the plurality of processor cores.

A caching system including a first sub-cache and a second sub-cache in parallel with the first sub-cache, wherein the second sub-cache includes a set of cache lines, line type bits configured to store an indication that a corresponding cache line of the set of cache lines is configured to store write-miss data, and an eviction controller configured to flush stored write-miss data based on the line type bits.

Embodiments are generally directed to methods and apparatuses for dynamically changing data priority in a cache. An embodiment of an apparatus comprising: a priority controller to: receive a memory access request to request data; and set a priority flag for the memory access request based on an accumulated access amount of data stored in a memory block to be accessed by the memory access request to dynamically change a priority level of the requested data.

A method and system for sharing memory between a central processing unit (CPU) and a graphics processing unit (GPU) of a computing device are disclosed herein. The method includes allocating a surface within a physical memory and mapping the surface to a plurality of virtual memory addresses within a CPU page table. The method also includes mapping the surface to a plurality of graphics virtual memory addresses within an I/O device page table.

Systems and methods for efficient metadata processing, for example, by resolving input patterns into binary representations ahead of time. In some embodiments, a plurality of input patterns may be identified, wherein an input pattern of the plurality of input patterns comprises a metadata label. A plurality of respective values may be selected for a plurality of variables, wherein the plurality of variables comprise a variable corresponding to the metadata label of the input pattern. A binary representation of the metadata label may be obtained based on the respective value of the variable.