Abstract: Various methods are provided directed to a compiler-guided software accelerator for iterative HADOOP® jobs. A method includes identifying intermediate data, generated by an iterative HADOOP® application, below a predetermined threshold size and used less than a predetermined threshold time period. The intermediate data is stored in a memory device. The method further includes minimizing input, output, and synchronization overhead for the intermediate data by selectively using at any given time any one of a Message Passing Interface and Distributed File System as a communication layer. The Message Passing Interface is co-located with the HADOOP® Distributed File System.

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 guarante

Abstract: Systems and methods are disclosed that share coprocessor resources between two or more applications in a computing cluster using a job selector to receive jobs from a job queue; a node selector coupled to the job selector; an off line profiler with an interference prediction model; a coprocessor dynamic interference detection module; and a coprocessor interference response module.

Abstract: Systems and methods for automatic generation of software pipelines for heterogeneous parallel systems (AHP) include pipelining a program with one or more tasks on a parallel computing platform with one or more processing units and partitioning the program into pipeline stages, wherein each pipeline stage contains one or more tasks. The one or more tasks in the pipeline stages are scheduled onto the one or more processing units, and execution times of the one or more tasks in the pipeline stages are estimated. The above steps are repeated until a specified termination criterion is reached.

Abstract: Systems and methods for managing a processor and one or more co-processors for a database application whose queries have been processed into an intermediate form (IR) containing kernels of the database application that have been fused and split; dynamically scheduling such kernels on CUDA streams and further dynamically dispatching kernels to GPU devices by estimating execution time in order to achieve high performance.

Abstract: A system for dynamically binding and unbinding of graphics processing unit GPU applications, the system includes a memory management for tracking memory of a GPU used by an application, and a source-to-source compiler for identifying nested structures allocated on the GPU so that the virtual memory management can track these nested structures, and identifying all instances where nested structures on the GPU are modified inside kernels.

Abstract: Low-power systems and methods are disclosed for executing an application software on a general purpose processor and a plurality of accelerators with a runtime controller. The runtime controller splits a workload across the processor and the accelerators to minimize energy. The system includes building one or more performance models in an application-agnostic manner; and monitoring system performance in real-time and adjusting the workload splitting to minimize energy while conforming to a target quality of service (QoS).

Abstract: The invention solves the problem of sharing many-core devices (e.g. GPUs) among concurrent applications running on heterogeneous clusters. In particular, the invention provides transparent mapping of applications to many-core devices (that is, the user does not need to be aware of the many-core devices present in the cluster and of their utilization), time-sharing of many-core devices among applications also in the presence of conflicting memory requirements, and dynamic binding/binding of applications to/from many-core devices (that is, applications do not need to be statically mapped to the same many-core device for their whole life-time).

Abstract: A method for compiler-guided optimization of MapReduce type applications that includes applying transformations and optimizations to JAVA bytecode of an original application by an instrumenter which carries out static analysis to determine application properties depending on the optimization being performed and provides an output of optimized JAVA bytecode, and executing the application and analyzing generated trace and feeds information back into the instrumenter by a trace analyzer, the trace analyzer and instrumenter invoking each other iteratively and exchanging information through files.

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 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 guarante

Abstract: Various methods are provided directed to a compiler-guided software accelerator for iterative HADOOP jobs. A method includes identifying intermediate data, generated by an iterative HADOOP application, below a predetermined threshold size and used less than a predetermined threshold time period. The intermediate data is stored in a memory device. The method further includes minimizing input, output, and synchronization overhead for the intermediate data by selectively using at any given time any one of a Message Passing Interface and Distributed File System as a communication layer. The Message Passing Interface is co-located with the HADOOP Distributed File System.

Abstract: Systems and methods for automatic generation of software pipelines for heterogeneous parallel systems (AHP) include pipelining a program with one or more tasks on a parallel computing platform with one or more processing units and partitioning the program into pipeline stages, wherein each pipeline stage contains one or more tasks. The one or more tasks in the pipeline stages are scheduled onto the one or more processing units, and execution times of the one or more tasks in the pipeline stages are estimated. The above steps are repeated until a specified termination criterion is reached.

Abstract: Systems and methods are disclosed that share coprocessor resources between two or more applications in a computing cluster using a job selector to receive jobs from a job queue; a node selector coupled to the job selector; an off line profiler with an interference prediction model; a coprocessor dynamic interference detection module; and a coprocessor interference response module.

Abstract: A method for compiler-guided optimization of MapReduce type applications that includes applying transformations and optimizations to Java bytecode of an original application by an instrumenter which carries out static analysis to determine application properties depending on the optimization being performed and provides an output of optimized Java bytecode, and executing the application and analyzing generated trace and feeds information back into the instrumenter by a trace analyzer, the trace analyzer and instrumenter invoking each other iteratively and exchanging information through files.

Abstract: Systems and methods for managing a processor and one or more co-processors for a database application whose queries have been processed into an intermediate form (IR) containing kernels of the database application that have been fused and split; dynamically scheduling such kernels on CUDA streams and further dynamically dispatching kernels to GPU devices by estimating execution time in order to achieve high performance.

Abstract: A method system for training an apparatus to recognize a pattern includes providing the apparatus with a host processor executing steps of a machine learning process; providing the apparatus with an accelerator including at least two processors; inputting training pattern data into the host processor; determining coefficient changes in the machine learning process with the host processor using the training pattern data; transferring the training data to the accelerator; determining kernel dot-products with the at least two processors of the accelerator using the training data; and transferring the dot-products back to the host processor.

Abstract: A system for dynamically binding and unbinding of graphics processing unit GPU applications, the system includes a memory management for tracking memory of a GPU used by an application, and a source-to-source compiler for identifying nested structures allocated on the GPU so that the virtual memory management can track these nested structures, and identifying all instances where nested structures on the GPU are modified inside kernels.

Abstract: The invention solves the problem of sharing many-core devices (e.g. GPUs) among concurrent applications running on heterogeneous clusters. In particular, the invention provides transparent mapping of applications to many-core devices (that is, the user does not need to be aware of the many-core devices present in the cluster and of their utilization), time-sharing of many-core devices among applications also in the presence of conflicting memory requirements, and dynamic binding/binding of applications to/from many-core devices (that is, applications do not need to be statically mapped to the same many-core device for their whole life-time).