Abstract: Methods and apparatus for managing circular queues are disclosed. A pointer designates an index position of a particular queue element and contains an additional pointer state, whereby two pointer values (split indexes) can designate the same index position. Front and rear pointers are respectively managed by dequeue and enqueue logic. The front pointer state and rear pointer state distinguish full and empty queue states when both pointers designate the same index position. Asynchronous dequeue and enqueue operations are supported, no lock is required, and no queue entry is wasted. Hardware and software embodiments for numerous applications are disclosed.

Abstract: Apparatus and methods are disclosed for scheduling tasks in a heterogeneous computing environment. Coarse scheduling of a received task-set is performed centrally, with tasks dispatched to respective processing resources including one or more accelerators. At each accelerator, sub-tasks of a received task are identified, scheduled, and executed. Data-transfer and computation sub-tasks can be pipelined. The accelerator operates using small tiles of local data, which are transferred to or from a large shared reservoir of main memory. Sub-task scheduling can be customized to each accelerator; coarse task scheduling can work on larger tasks; both can be efficient. Simulations demonstrate large improvements in makespan and/or circuit area. Disclosed technologies are scalable and can be implemented in varying combinations of hard-wired or software modules. These technologies are widely applicable to high-performance computing, image classification, media processing, wireless coding, encryption, and other fields.

Abstract: Methods and apparatus for managing circular queues are disclosed. A pointer designates an index position of a particular queue element and contains an additional pointer state, whereby two pointer values (split indexes) can designate the same index position. Front and rear pointers are respectively managed by dequeue and enqueue logic. The front pointer state and rear pointer state distinguish full and empty queue states when both pointers designate the same index position. Asynchronous dequeue and enqueue operations are supported, no lock is required, no queue entry is wasted. Hardware and software embodiments for numerous applications are disclosed.

Abstract: The method and system described herein presents a technique for performance analysis that helps users understand the communication behavior of their message passing applications. The method and system described herein may automatically classifies individual communication operations and reveal the cause of communication inefficiencies in the application. This classification allows the developer to quickly focus on the culprits of truly inefficient behavior, rather than manually foraging through massive amounts of performance data. Specifically, the method and system described herein trace the message operations of Message Passing Interface (MPI) applications and then classify each individual communication event using a supervised learning technique: decision tree classification. The decision tree may be trained using microbenchmarks that demonstrate both efficient and inefficient communication.

Abstract: The method and system described herein presents a technique for performance analysis that helps users understand the communication behavior of their message passing applications. The method and system described herein may automatically classifies individual communication operations and reveal the cause of communication inefficiencies in the application. This classification allows the developer to quickly focus on the culprits of truly inefficient behavior, rather than manually foraging through massive amounts of performance data. Specifically, the method and system described herein trace the message operations of Message Passing Interface (MPI) applications and then classify each individual communication event using a supervised learning technique: decision tree classification. The decision tree may be trained using microbenchmarks that demonstrate both efficient and inefficient communication.