Abstract: In a first embodiment of the present invention, a method for enabling hardware acceleration of web applications is provided, comprising: parsing a web page using a scripting engine, wherein the web page necessitates running a web application; accessing one or more Application Program Interfaces (APIs) that provide parallelization, and distribute tasks of the web application among multiple cores of a multi-core central processing unit (CPU) or graphical processing unit (GPU), wherein the accessing uses a compute context class that, when instantiated, creates a compute context object that acts as a bridge between the scripting engine and the one or more APIs; and creating one or more kernels to operate on the multiple cores.

Abstract: In a first embodiment of the present invention, a method for enabling hardware acceleration of web applications is provided, comprising: parsing a web page using a scripting engine, wherein the web page necessitates running a web application; accessing one or more Application Program Interfaces (APIs) that provide parallelization, and distribute tasks of the web application among multiple cores of a multi-core central processing unit (CPU) or graphical processing unit (GPU), wherein the accessing uses a compute context class that, when instantiated, creates a compute context object that acts as a bridge between the scripting engine and the one or more APIs; and creating one or more kernels to operate on the multiple cores.

Abstract: In a first embodiment of the present invention, a method for enabling hardware acceleration of web applications is provided, comprising: parsing a web page using a scripting engine, wherein the web page necessitates running a web application; accessing one or more Application Program Interfaces (APIs) that provide parallelization, and distribute tasks of the web application among multiple cores of a multi-core central processing unit (CPU) or graphical processing unit (GPU), wherein the accessing uses a compute context class that, when instantiated, creates a compute context object that acts as a bridge between the scripting engine and the one or more APIs; and creating one or more kernels to operate on the multiple cores.

Abstract: An image represented by multiple nodes can be processed by determining whether information can be propagated to a node from another node (e.g., source node) of the image, thereby allowing significantly greater parallelism and scalability by taking advantage of multiprocessing or multi-core processors that are prevalent and widely available today. Conceptually, an image can be presented as a “structured grid” of multiple nodes (e.g., a structured grid of pixels of an image). In a “structured grid,” two or more of the nodes can determine whether to propagate information in parallel. In fact, each node of a “structured grid” can perform operations relating to propagation of information in parallel. This means that for an image of N pixels, it is possible to perform N operations in parallel. It is also possible to divide the processing of N operations for N pixels substantially equally between the number processors or processing cores available at a given time.

Abstract: An image represented by multiple nodes can be processed by determining whether labels can be propagated to a node from another node of the image. Conceptually, an image can be presented as a “structured grid” of multiple nodes (e.g., a structured grid of pixels of an image). In a “structured grid,” two or more nodes of the same level (e.g., nodes in the same gray level) can determine in parallel whether to propagate a label from one or more of its neighboring nodes that are labeled and propagate one or more labels accordingly. An image can be processed by iteratively repeating this process for nodes of successive levels. It will be appreciated that the disclosed techniques allow parallelism without requiring partitioning of an image or having to merge partitioned images. The disclosed techniques are especially suited for watershed algorithms.

Abstract: In a first embodiment of the present invention, a method for enabling hardware acceleration of web applications is provided, comprising: parsing a web page using a scripting engine, wherein the web page necessitates running a web application; accessing one or more Application Program Interfaces (APIs) that provide parallelization, and distribute tasks of the web application among multiple cores of a multi-core central processing unit (CPU) or graphical processing unit (GPU), wherein the accessing uses a compute context class that, when instantiated, creates a compute context object that acts as a bridge between the scripting engine and the one or more APIs; and creating one or more kernels to operate on the multiple cores.

Abstract: An image represented by multiple nodes can be processed by determining whether information can be propagated to a node from another node (e.g., source node) of the image, thereby allowing significantly greater parallelism and scalability by taking advantage of multiprocessing or multi-core processors that are prevalent and widely available today. Conceptually, an image can be presented as a “structured grid” of multiple nodes (e.g., a structured grid of pixels of an image). In a “structured grid,” two or more of the nodes can determine whether to propagate information in parallel. In fact, each node of a “structured grid” can perform operations relating to propagation of information in parallel. This means that for an image of N pixels, it is possible to perform N operations in parallel. It is also possible to divide the processing of N operations for N pixels substantially equally between the number processors or processing cores available at a given time.

Abstract: An image represented by multiple nodes can be processed by determining whether labels can be propagated to a node from another node of the image. Conceptually, an image can be presented as a “structured grid” of multiple nodes (e.g., a structured grid of pixels of an image). In a “structured grid,” two or more nodes of the same level (e.g., nodes in the same gray level) can determine in parallel whether to propagate a label from one or more of its neighboring nodes that are labeled and propagate one or more labels accordingly. An image can be processed by iteratively repeating this process for nodes of successive levels. It will be appreciated that the disclosed techniques allow parallelism without requiring partitioning of an image or having to merge partitioned images. The disclosed techniques are especially suited for watershed algorithms.