Abstract: Techniques and mechanisms for sensing a voltage difference across two interconnect structures of a multi-chip packaged device. In an embodiment, the interconnect structures provide respective voltages to each of multiple integrated circuit (IC) chips of the packaged device. Switch circuitry of the packaged device is operable to provide any of multiple modes which each switchedly couple a voltage sensor to a different respective one of various sample point pairs of the interconnect structures. Control circuitry operates the switch circuitry to selectively provide one of the multiple modes based on an indication of a workload to be performed with one or more of the IC chips. In another embodiment, the voltage sensor senses the voltages each at a respective sample point of a sample point pair which corresponds to the selected mode of the switch circuitry.

Abstract: Methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement software defined silicon feature configuration pay-as-you-go licensing are disclosed. A disclosed silicon semiconductor device includes a first counter that increments a first count when a timer expires and, responsive to expiration of the timer, a feature configuration sampler to sample a state of a configuration of a feature of the silicon semiconductor device. In addition, the silicon semiconductor device includes a second counter that increments a second count when the sampled state of the configuration of the feature indicates the feature is active. A feature up-time tracker is also included outputs a value representative of an amount of time the configuration has been active, where the amount of time is based on the first count and the second count.

Abstract: Example method and apparatus, systems, and articles of manufacture for immersion cooling systems are disclosed herein. An example apparatus disclosed herein includes a tank to hold a coolant, an overflow chamber to direct the coolant toward an outlet, and a plate within the overflow chamber, the plate including a plurality of openings, the coolant to pass through at least one of the plurality of openings before reaching the outlet.

Abstract: Technologies for allocating resources of managed nodes to workloads to balance multiple resource allocation objectives include an orchestrator server to receive resource allocation objective data indicative of multiple resource allocation objectives to be satisfied. The orchestrator server is additionally to determine an initial assignment of a set of workloads among the managed nodes and receive telemetry data from the managed nodes. The orchestrator server is further to determine, as a function of the telemetry data and the resource allocation objective data, an adjustment to the assignment of the workloads to increase an achievement of at least one of the resource allocation objectives without decreasing an achievement of another of the resource allocation objectives, and apply the adjustments to the assignments of the workloads among the managed nodes as the workloads are performed. Other embodiments are also described and claimed.

Abstract: Disclosed embodiments are related to techniques for powering compute platforms in low temperature environments. Embodiments include a preheating stage that is added to a power up sequence. The preheating stage may include a force-on stage and a force-offstage. During the force-on stage, all power rails of target components are forced to an ON state so that the target components consume current. When a target operating temperature is reached, the power rails of the target components are turned off, which causes the target components to revert back to their initial (pre-boot) state allowing the normal boot process to take place. Since the target components are now heated up, the boot process can execute faster than when the target components were cold. Other embodiments may be described and/or claimed.

Abstract: Technologies for predicting computer hardware performance with machine learning are disclosed. Analysis of telemetry data through machine learning and statistical modeling is used to determine whether various components of a compute device such as a fan or memory are failing or are otherwise potentially impacting performance of the compute device. For example, machine-learning-based algorithms may be used to determine an impact of a latency of memory accesses may have on time to execute workloads.

Abstract: Two liquid cooling mechanisms are provided for cooling integrated circuit components immersed in an open bath immersion tank. In the first mechanism, heat generated by high-thermal design power (TDP) components is absorbed by a working fluid passing through cold plates coupled to the high-TDP components. The cold plates are part of direct liquid cooling loops attached to supply and return manifolds fluidly connected to a cooling distribution unit. In the second mechanism, integrated circuit components not coupled to any of the direct liquid cooling loops dissipate heat directly to the immersion fluid. In some embodiments, the tank is a closed bath immersion tank and heat captured by the working fluid is reclaimed and converted to electricity. Working fluid flow rate can be adjusted based on integrated circuit component power consumption levels to achieve a desired working fluid temperature as it enters an energy reclamation unit.

Abstract: Technologies for allocating resources of managed nodes to workloads to balance multiple resource allocation objectives include an orchestrator server to receive resource allocation objective data indicative of multiple resource allocation objectives to be satisfied. The orchestrator server is additionally to determine an initial assignment of a set of workloads among the managed nodes and receive telemetry data from the managed nodes. The orchestrator server is further to determine, as a function of the telemetry data and the resource allocation objective data, an adjustment to the assignment of the workloads to increase an achievement of at least one of the resource allocation objectives without decreasing an achievement of another of the resource allocation objectives, and apply the adjustments to the assignments of the workloads among the managed nodes as the workloads are performed. Other embodiments are also described and claimed.

Abstract: Technologies for allocating resources of managed nodes to workloads to balance multiple resource allocation objectives include an orchestrator server to receive resource allocation objective data indicative of multiple resource allocation objectives to be satisfied. The orchestrator server is additionally to determine an initial assignment of a set of workloads among the managed nodes and receive telemetry data from the managed nodes. The orchestrator server is further to determine, as a function of the telemetry data and the resource allocation objective data, an adjustment to the assignment of the workloads to increase an achievement of at least one of the resource allocation objectives without decreasing an achievement of another of the resource allocation objectives, and apply the adjustments to the assignments of the workloads among the managed nodes as the workloads are performed. Other embodiments are also described and claimed.

Abstract: Technologies for dynamically allocating resources among a set of managed nodes include an orchestrator server to receive telemetry data from the managed nodes indicative of resource utilization and workload performance by the managed nodes as the workloads are executed, generate a resource allocation map indicative of allocations of resources among the managed nodes, determine, as a function of the telemetry data and the resource allocation map, a dynamic adjustment to allocation of resources to at least one of the managed nodes to improve performance of at least one of the workloads executed on the at least one of the managed nodes, and apply the adjustment to the allocation of the resources among the managed nodes as the workloads are executed. Other embodiments are also described and claimed.

Abstract: Technologies for allocating resources of a set of managed nodes to workloads to manage heat generation include an orchestrator server to receive resource allocation objective data including a target temperature for one or more of the managed nodes. The orchestrator server is also to determine an initial assignment of a set of workloads among the managed nodes, receive telemetry data from the managed nodes indicative of resource utilization by each of the managed nodes and one or more temperatures and fan speeds of the managed nodes as the workloads are performed, predict future heat generation of the workloads as a function of the telemetry data, determine, as a function of the predicted future heat generation, an adjustment to the assignment of the workloads to achieve the target temperature, and apply the adjustments to the assignments of the workloads among the managed nodes as the workloads are performed.

Abstract: A method for determining whether to perform maintenance for an electronic device includes generating a baseline characterization of thermal performance for a heat-generating component of the electronic device at a baseline date. The method also includes generating an assessment characterization of the thermal performance at an assessment date after the baseline date. The method further includes generating a historical trend that includes the baseline characterization and the assessment characterization. Additionally, the method includes determining whether to perform maintenance for the heat-generating component based on the historical trend and a specified maintenance parameter.

Abstract: Technologies for allocating resources of a set of managed nodes to workloads based on resource utilization phase residencies include an orchestrator server to receive resource allocation objective data and determine an assignment of a set of workloads among the managed nodes. The orchestrator server is further to receive telemetry data from the managed nodes, determine, as a function of the telemetry data, phase residency data, determine, as a function of at least the phase residency data and the resource allocation objective data, an adjustment to the assignment of the workloads to increase an achievement of at least one of the resource allocation objectives without decreasing the achievement of any of the other resource allocation objectives, and apply the adjustment to the assignments of the workloads among the managed nodes as the workloads are performed.

Abstract: One embodiment provides an apparatus. The apparatus includes power thermal awareness solution (PTAS) logic to select a model from a plurality of models based, at least in part, on a configuration of a cooling zone; and determine a cooling zone volumetric airflow based, at least in part, on the selected model.

Abstract: Technologies for assigning workloads based on resource utilization phases include an orchestrator server to assign a set of workloads to the managed nodes. The orchestrator server is also to receive telemetry data from the managed nodes and identify, as a function of the telemetry data, historical resource utilization phases of the workloads. Further, the orchestrator server is to determine, as a function of the historical resource utilization phases and as the workloads are performed, predicted resource utilization phases for the workloads, and apply, as a function of the predicted resources utilization phases, adjustments to the assignments of the workloads among the managed nodes as the workloads are performed.

Abstract: Technologies for predicting computer hardware performance with machine learning are disclosed. Analysis of telemetry data through machine learning and statistical modeling is used to determine whether various components of a compute device such as a fan or memory are failing or are otherwise potentially impacting performance of the compute device. For example, machine-learning-based algorithms may be used to determine an impact of a latency of memory accesses may have on time to execute workloads.

Abstract: Technologies for predicting the power usage of a data center are disclosed. A data center manager gathers sensor data from the compute devices of the data center. The sensor data indicates factors such as power used by the compute device and the intake air inlet temperature. The data center manager trains a machine-learning-based algorithm based on training sensor data, and then applies the machine-learning-based algorithm to sensor data as it is being gathered. The machine-learning-based algorithm can predict a change in future power usage of the data center, and control a cooling unit to compensate before the power usage even begins to change.

Abstract: Examples may include techniques to a schedule a workload to one or more computing resources of a data center. A class is determined for the workload based on a workload type or profile for the workload. Predicted operating values for at least one of the one or more computing resources is determined based on the class and the predicted operating values are used as inputs in at least one scoring model to evaluate the workload being supported by the at least one of the one or more computing resources. The workload is then scheduled to the at least one or more computing resources based on the evaluation.

Abstract: One embodiment provides an apparatus. The apparatus includes power thermal awareness solution (PTAS) logic to select a model from a plurality of models based, at least in part, on a configuration of a cooling zone; and determine a cooling zone volumetric airflow based, at least in part, on the selected model.

Abstract: Technologies for determining and storing workload characteristics include an orchestrator server to identify a workload to be executed by a managed node, obtain a profile associated with the workload, wherein the profile includes a model that relates an input parameter set indicative of one of more characteristics of the workload with an output parameter set indicative of one or more aspects of resources to be allocated for execution of the workload, determine, as a function of the input parameter set and the model, resources to allocate to the managed node to execute the workload, and allocate the determined resources to the managed node to execute the workload. Other embodiments are also described and claimed.