Engineering topics
Publications and source records attributed to Arora, Manish.
A plurality of thermal electric cooler (TEC) elements are formed in a TEC grid structure. Control logic dynamically varies a supply current supplied to each TEC element (or group of TEC elements) in the TEC grid based on changes in power density respectively associated with areas cooled by each of the TEC elements or group of TEC elements.
Systems, apparatuses, and methods for managing variations among nodes in parallel system frameworks. Sensor and performance data associated with the nodes of a multi-node cluster may be monitored to detect variations among the nodes. A variability metric may be calculated for each node of the cluster based on the sensor and performance data associated with the node. The variability metrics may then be used by a mapper to efficiently map tasks of a parallel application to the nodes of the cluster. In one embodiment, the mapper may assign the critical tasks of the parallel application to the nodes with the lowest variability metrics. In another embodiment, the hardware of the nodes may be reconfigured so as to reduce the node-to-node variability.
A method includes controlling active frequency states of a plurality of heterogeneous processing units based on frequency sensitivity metrics indicating performance coupling between different types of processing units in the plurality of heterogeneous processing units. A processor includes a plurality of heterogeneous processing units and a performance controller to control active frequency states of the plurality of heterogeneous processing units based on frequency sensitivity metrics indicating performance coupling between different types of processing units in the plurality of heterogeneous processing units. The active frequency state of a first type of processing unit in the plurality of heterogeneous processing units is controlled based on a first activity metric associated with a first type of processing unit and a second activity metric associated with a second type of processing unit.
Methods, devices, and systems for capturing an accuracy of an instruction executing on a processor. An instruction may be executed on the processor, and the accuracy of the instruction may be captured using a hardware counter circuit. The accuracy of the instruction may be captured by analyzing bits of at least one value of the instruction to determine a minimum or maximum precision datatype for representing the field, and determining whether to adjust a value of the hardware counter circuit accordingly. The representation may be output to a debugger or logfile for use by a developer, or may be output to a runtime or virtual machine to automatically adjust instruction precision or gating of portions of the processor datapath.
In the past two decades, numerous scheduling and load balancing techniques have been proposed for locally distributed multiprocessor systems. However, they all suffer from significant deficiencies when extended to a Grid environment: some use a centralized approach that renders the algorithm unscalable, while others assume the overhead involved in searching for appropriate resources to be negligible. Furthermore, classical scheduling algorithms do not consider a Grid node to be N-resource rich and merely work towards maximizing the utilization of one of the resources. In this paper we propose a new scheduling and load balancing algorithm for a generalized Grid model of N-resource nodes that not only takes into account the node and network heterogeneity, but also considers the overhead involved in coordinating among the nodes. Our algorithm is de-centralized, scalable, and overlaps the node coordination time of the actual processing of ready jobs, thus saving valuable clock cycles needed for making decisions. The proposed algorithm is studied by conducting simulations using the Message Passing Interface (MPI) paradigm.
In the past two decades, numerous scheduling and load balancing techniques have been proposed for locally distributed multiprocessor systems. However, they all suffer from significant deficiencies when extended to a Grid environment: some use a centralized approach that renders the algorithm unscalable, while others assume the overhead involved in searching for appropriate resources to be negligible. Furthermore, classical scheduling algorithms do not consider a Grid node to be N-resource rich and merely work towards maximizing the utilization of one of the resources. In this paper, we propose a new scheduling and load balancing algorithm for a generalized Grid model of N-resource nodes that not only takes into account the node and network heterogeneity, but also considers the overhead involved in coordinating among the nodes. Our algorithm is decentralized, scalable, and overlaps the node coordination time with that of the actual processing of ready jobs, thus saving valuable clock cycles needed for making decisions. The proposed algorithm is studied by conducting simulations using the Message Passing Interface (MPI) paradigm.