Abstract:
Systems and methods for swapping out and in pinned memory regions between main memory and a separate storage location in a system, including establishing an offload buffer in an interposing library; swapping out pinned memory regions by transferring offload buffer data from a coprocessor memory to a host processor memory, unregistering and unmapping a memory region employed by the offload buffer from the interposing library, wherein the interposing library is pre-loaded on the coprocessor, and collects and stores information employed during the swapping out. The pinned memory regions are swapped in by mapping and re-registering the files to the memory region employed by the offload buffer, and transferring data of the offload buffer data from the host memory back to the re-registered memory region.
Abstract:
A method is disclosed to manage a multi-processor system with one or more multiple-core coprocessors by intercepting coprocessor offload infrastructure application program interface (API) calls; scheduling user processes to run on one of the coprocessors; scheduling offloads within user processes to run on one of the coprocessors; and affinitizing offloads to predetermined cores within one of the coprocessors by selecting and allocating cores to an offload, and obtaining a thread-to-core mapping from a user.
Abstract:
A method is disclosed to manage a multi-processor system with one or more manycore devices, by managing real-time bag-of-tasks applications for a cluster, wherein each task runs on a single server node, and uses the offload programming model, and wherein each task has a deadline and three specific resource requirements: total processing time, a certain number of manycore devices and peak memory on each device; when a new task arrives, querying each node scheduler to determine which node can best accept the task and each node scheduler responds with an estimated completion time and a confidence level, wherein the node schedulers use an urgency-based heuristic to schedule each task and its offloads; responding to an accept/reject query phase, wherein the cluster scheduler send the task requirements to each node and queries if the node can accept the task with an estimated completion time and confidence level; and scheduling tasks and offloads using a aging and urgency-based heuristic, wherein the aging guarantees fairness, and the urgency prioritizes tasks and offloads so that maximal deadlines are met.
Abstract:
Methods and systems for scheduling jobs to manycore nodes in a cluster include selecting a job to run according to the job's wait time and the job's expected execution time; sending job requirements to all nodes in a cluster, where each node includes a manycore processor; determining at each node whether said node has sufficient resources to ever satisfy the job requirements and, if no node has sufficient resources, deleting the job; creating a list of nodes that have sufficient free resources at a present time to satisfy the job requirements; and assigning the job to a node, based on a difference between an expected execution time and associated confidence value for each node and a hypothetical fastest execution time and associated hypothetical maximum confidence value.
Abstract:
Methods and systems for scheduling jobs to manycore nodes in a cluster include selecting a job to run according to the job's wait time and the job's expected execution time; sending job requirements to all nodes in a cluster, where each node includes a manycore processor; determining at each node whether said node has sufficient resources to ever satisfy the job requirements and, if no node has sufficient resources, deleting the job; creating a list of nodes that have sufficient free resources at a present time to satisfy the job requirements; and assigning the job to a node, based on a difference between an expected execution time and associated confidence value for each node and a hypothetical fastest execution time and associated hypothetical maximum confidence value.
Abstract:
Systems and methods for swapping out and in pinned memory regions between main memory and a separate storage location in a system, including establishing an offload buffer in an interposing library; swapping out pinned memory regions by transferring offload buffer data from a coprocessor memory to a host processor memory, unregistering and unmapping a memory region employed by the offload buffer from the interposing library, wherein the interposing library is pre-loaded on the coprocessor, and collects and stores information employed during the swapping out. The pinned memory regions are swapped in by mapping and re-registering the files to the memory region employed by the offload buffer, and transferring data of the offload buffer data from the host memory back to the re-registered memory region.
Abstract:
A runtime method is disclosed that dynamically sets up core containers and thread-to-core affinity for processes running on manycore coprocessors. The method is completely transparent to user applications and incurs low runtime overhead. The method is implemented within a user-space middleware that also performs scheduling and resource management for both offload and native applications using the manycore coprocessors.
Abstract:
A runtime method is disclosed that dynamically sets up core containers and thread-to-core affinity for processes running on manycore coprocessors. The method is completely transparent to user applications and incurs low runtime overhead. The method is implemented within a user-space middleware that also performs scheduling and resource management for both offload and native applications using the manycore coprocessors.
Abstract:
A method is disclosed to manage a multi-processor system with one or more manycore devices, by managing real-time bag-of-tasks applications for a cluster, wherein each task runs on a single server node, and uses the offload programming model, and wherein each task has a deadline and three specific resource requirements: total processing time, a certain number of manycore devices and peak memory on each device; when a new task arrives, querying each node scheduler to determine which node can best accept the task and each node scheduler responds with an estimated completion time and a confidence level, wherein the node schedulers use an urgency-based heuristic to schedule each task and its offloads; responding to an accept/reject query phase, wherein the cluster scheduler send the task requirements to each node and queries if the node can accept the task with an estimated completion time and confidence level; and scheduling tasks and offloads using a aging and urgency-based heuristic, wherein the aging guarantees fairness, and the urgency prioritizes tasks and offloads so that maximal deadlines are met.