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An approach is disclosed that configures a computer system node from components that are each connected to an intra-node network. The configuring is performed by selecting a set of components, including at least one processor, and assigning each of the components a different address range within the node. An operating system is run on the processor included in the node with the operating system accessing each of the assigned components.
Chemical processors are configured to reduce mass, work in conjunction with solar concentrators, and/or house porous inserts in microchannel or mesochannel devices made by additive manufacturing. Methods of making chemical processors containing porous inserts by additive manufacturing are also disclosed.
A processing device is provided which includes memory comprising data cache memory configured to store compressed data and metadata cache memory configured to store metadata, each portion of metadata comprising an encoding used to compress a portion of data. The processing device also includes at least one processor configured to compress portions of data and select, based on one or more utility level metrics, portions of metadata to be stored in the metadata cache memory. The at least one processor is also configured to store, in the metadata cache memory, the portions of metadata selected to be stored in the metadata cache memory, store, in the data cache memory, each portion of compressed data having a selected portion of corresponding metadata stored in the metadata cache memory. Each portion of compressed data, having the selected portion of corresponding metadata stored in the metadata cache memory, is decompressed.
A system for enhanced power system model validation is provided. The system includes a computing device including at least one processor in communication with at least one memory device. The at least one processor is programmed to store a plurality of models for a plurality of devices and a plurality of input files associated with the plurality of models, receive, from a user, a selection of model of the plurality of models to simulate, retrieve one or more input files of the plurality of input files, perform a model validity check on the selected model, if the selected model passed the model validity check, perform a model calibration on the selected model, and if the selected model passed the model calibration, perform a post evaluation on the selected model.
A multichannel angular spectrometer includes an array of fiber pickups having an arcuate arrangement and focused about a sample volume. A broadband light source is configured to illuminate a sample within the sample volume. At least one dispersion element is in optical communication with the array of fiber pickups. An imaging sensor is in optical communication with the array of fiber pickups. The imaging sensor is configured to image the broadband light received by the array of fiber pickups and dispersed by the at least one dispersion element. A processor is in electrical communication with the imaging sensor. The processor has a power supply and computer-readable memory.
Today the LHC offline computing relies heavily on CPU resources, despite the interest in compute accelerators, such as GPUs, for the longer term future. The number of cores per CPU socket has continued to increase steadily, reaching the levels of 64 cores (128 threads) with recent AMD EPYC processors, and 128 cores on Ampere Altra Max ARM processors. Over the course of the past decade, the CMS data processing framework, CMSSW, has been transformed from a single-threaded framework into a highly concurrent one. The first multithreaded version was brought into production by the start of the LHC Run 2 in 2015. Since then, the framework's threading efficiency has gradually been improved by adding more levels of concurrency and reducing the amount of serial code paths. The latest addition was support for concurrent Runs. In this work we review the concurrency model of the CMSSW, and measure its scalability with real CMS applications, such as simulation and reconstruction, on mode rn many-core machines. We show metrics such as event processing throughput and application memory usage with and without the contribution of I/O, as I/O has been the major scaling limitation for the CMS applications.
A processing unit includes one or more processor cores and a set of registers to store configuration information for the processing unit. The processing unit also includes a coprocessor configured to receive a request to modify a memory allocation for a kernel concurrently with the kernel executing on the at least one processor core. The coprocessor is configured to modify the memory allocation by modifying the configuration information stored in the set of registers. In some cases, initial configuration information is provided to the set of registers by a different processing unit. The initial configuration information is stored in the set of registers prior to the coprocessor modifying the configuration information.
In some embodiments, a memory controller in a processor includes a base value cache, a compressor, and a metadata cache. The compressor is coupled to the base value cache and the metadata cache. The compressor compresses a data block using at least a base value and delta values. The compressor determines whether the size of the data block exceeds a data block threshold value. Based on the determination of whether the size of the compressed data block generated by the compressor exceeds the data block threshold value, the memory controller transfers only a set of the compressed delta values to memory for storage. A decompressor located in the lower level cache of the processor decompresses the compressed data block using the base value stored in the base value cache, metadata stored in the metadata cache and the delta values stored in memory.
A sensor system includes a sensing element having a section of a layer assembly deposited onto a substrate. The layer assembly includes plural layers of different materials. The section of the layer assembly is configured to be etched to form plural individual pillars of the plural layers of the different materials. The individual pillars are configured to be in contact with a fluid to sense one or more analyte fluid components within the fluid. The sensing element is configured to generate a sensor signal responsive to the individual pillars being in contact with the fluid. The sensor system includes one or more processors configured to receive the sensor signal from the sensing element. The one or more processors may identify the one or more analyte fluid components within the fluid and an amount of each of the analyte fluid components within the fluid based on the sensor signal.
The computation of dense matrix-matrix products (GEMMs) is central to many modeling and simulation workloads as well as AI/ML deep learning campaigns. In fact, millions of dollars are spent annually on computing GEMMs, and large model training demands are increasing exponentially. Specialized processors such as GPUs are designed to perform well for these operations. However, the performance of GEMMs on GPUs can exhibit complex behaviors depending on many factors, making it challenging to optimize the performance of GEMMs on these processors. In this study we undertake an examination of GEMM performance on several leading GPU models taken from product lines of GPUs to be deployed in forthcoming exascale computing systems. We show results to illustrate the many factors that can affect performance of GEMMs on GPUs. We then present data collected from a large number of test runs for an example GEMM operation to show the dependence behaviors of GEMM rate on matrix dimensions. Finally, we show results from machine learning-based performance models using novel feature engineering methods to fit the measured performance, providing a potential basis for GEMM performance tuning and autotuning methods for GPUs. Recommendations are also given for how to achieve high GEMM performance on modern GPUs.
To aid in WIPP PA calculations, the US DOE has undertaken a broad modernization effort that includes: 1) updating the hardware and operating system used; 2) moving the current PA (performance assessment) codes to the new system; and 3) adding a database to store results essential for the analysis of assessments; 4) integrating run-control scripts with a web site to facilitate monitoring of performance assessment calculations; and 5) adding new capabilities via implementation of the PFLOTRAN code to replace multiple codes currently in use. The majority of codes were migrated from an Open VMS/Alpha processor system to a Unix/Intel processor system. Most codes were upgraded to use the dynamic allocation features of Fortran 95 and double precision variables. A few codes were migrated from Linux/Intel systems to the Unix/Intel systems and these code required nearly no changes to the source code. All of the migrated codes were re-qualified under Sandia's WIPP QA program. The implementation of some of the conceptual models using PFLOTRAN is being driven by the expectation that changes in the configuration of the repository will require a 3-dimensional representation of the mined areas. Symmetry in the current configuration of the repository made a 2-D representation adequate. PFLOTRAN is an open-source, state-of-the-art massively parallel subsurface flow and reactive transport code. A version of this code is being developed specifically for WIPP PA in order to maintain the capabilities of the current flow and transport codes, as well as add the capabilities of a parallel code that will treat additional processes, including: 1) miscible multi-phase flow; and 2) heat transfer. PFLOTRAN will replace the two codes (i.e. BRAGFLO and NUTS) currently used for flow and transport calculations, simplifying the PA code structure and speeding up calculations by simultaneously solving for flow and transport with a single code. To ensure that PFLOTRAN reproduces the results of the current code suite, existing test cases have been used for verification purposes. Additional test cases have been developed to establish the new capabilities for WIPP PA. The result of the modernization effort will be a state-of-the-art subsurface flow and transport capability that will serve WIPP PA into the future. (authors)
Optimization of environmental restoration efforts at complex groundwater sites involves the development of a conceptual and often numerical flow and transport models. Typically, a limited number of simulations are done to assess the impact of parameter uncertainty, to evaluate remedial alternatives and develop a remedy, or to modify an existing remedy. A limited number of simulations are performed because each simulation can take upwards of several hours per simulation and until now the ability to conduct the 100's to 1000's of simulations needed to derive an optimal remedy was beyond the reach of the everyday practitioner. HGL has developed and implemented new parallel simulation and optimization methods in their software and has successfully deployed applications on high performance Cloud-based computing platforms. This advancement removes the impediment of burdensome and unpredictable runtime requirements these types of projects have typically exhibited using heuristic trial-and-error or even automated serial computation approaches. A parallel numerical solver has been added to the subsurface flow and transport simulator MODFLOW-SURFACT{sup TM} and has demonstrated reductions in solution time by as much as a factor of 9.8. This is accomplished by distributing the numerical computations of every time step over multiple processors. The integrated parallel simulation and optimization method uses the breadth and depth search approach; iteratively producing sets of tens or 100's of candidate solutions that are evaluated concurrently instead sequentially (i.e. one model evaluation at a time). Solutions within 80-100% of the globally optimal solution are regularly found expending only 5-10% of the usual computational effort using this new parallel optimization method (reducing computation effort by 10 to 20 times). Distributing optimal search runs across multiple processors additionally reduces calendar time to solutions. Practitioners evaluating multiple conceptual site models, model calibration by auto-calibration methods and remedial design by heuristics can enjoy significant reductions in model solution time by using the parallel numerical matrix solver in the physics-based simulator. Combining investigation and design analysis with breadth and depth parallel optimization methods, projects at the fringe of practical optimization, along with projects that were beyond the reach of formal optimization methods are now wholly feasible and tractable optimization problems. (authors)
A downhole multi-modality inspection system includes a first imaging device operable to generate first imaging data and a second imaging device operable to generate second imaging data. The first imaging device includes a first source operable to emit energy of a first modality, and a first detector operable to detect returning energy induced by the emitted energy of the first modality. The second imaging device includes a second source operable to emit energy of a second modality, and a second detector operable to detect returning energy induced by the emitted energy of the second modality. The system further includes a processor configured to receive the first imaging data and the second imaging data, and integrate the first imaging data with the second imaging data into an enhanced data stream. The processor correlates the first imaging data and the second imaging data to provide enhanced data for detecting potential wellbore anomalies.
Global fluence estimators may be calculated on accelerators and processors for neutron and photon Monte Carlo transport. Monte Carlo random walk simulation may be performed on the processors and the calculation of a Volumetric-Ray-Casting (VRC) estimator may be offloaded to the accelerators. The VRC estimator may modify an expected-value estimator to extend a pseudo-particle ray along the direction of the emitted particle from source and collision event through not only the event volume, but also through all volumes that describe the problem geometry. Additionally, many pseudo-particle rays may be sampled per event, rather than just a single pseudo-particle ray per event, in order to provide more complete angular coverage.
Traditional methods of accelerating Monte Carlo (MC) calculations typically use CPUs as the computing unit. The parallelism is based upon multithreading on a multi-core processor, and message passing interface (MPI) across many processors and nodes. In recent years, the hardware accelerators have been introduced by the high performance computing industry as an alternative approach to achieve massive parallelism. Hardware accelerators have the advantage of high Floating-point Operations Per Second (FLOPS) and high energy efficiency. Examples include graphics processing units (GPUs) and coprocessors. Compute-intensive programs can be off-loaded to them to be concurrently executed by hundreds or thousands of active threads. Existing production MC codes, however, cannot be directly compiled or executed on the new platforms. It is therefore necessary to rewrite or redevelop the code using the hardware accelerator specific programming models and APIs. Several studies have been conducted on using GPUs to accelerate MC calculations in reactor analysis and medical physics. In this paper, we present the recent progress at Rensselaer Polytechnic Institute (RPI) in developing a new parallel MC code called ARCHER (Accelerated Radiation-transport Computations in Heterogeneous Environments) for coupled photon-electron transport simulations. The code is designed as a test bed to evaluate the efficacy of heterogeneous computing systems for low-cost MC-based radiation dosimetric calculations. The code has three variants corresponding to three parallel hardware platforms: ARCHER-CPU (multi-core CPU), ARCHER-GPU (Nvidia GPU) and ARCHER-COP (Intel Xeon Phi coprocessor). The code has been used for several applications, including CT imaging dosimetry, radiotherapy dosimetry and radiation shielding design. (authors)
A radio-frequency method for range finding includes modulating a reference signal having an intermediate frequency to a downlink signal having a carrier frequency using a clock signal. The downlink signal is transmitted to a tag using a transceiver. An uplink signal backscattered front the tag is received and demodulated using the clock signal. The uplink signal has a frequency that is a harmonic of the carrier frequency. A distance between the tag and the transceiver is calculated based on a phase of the demodulated uplink signal. A system for range finding includes a transceiver and a processor. The transceiver modulates a reference signal to downlink signal and transmits the downlink signal. The transceiver receives and demodulates an uplink signal. The processor is configured to receive the demodulated uplink signal and calculate a distance between the tag and the transceiver using a phase of the demodulated uplink signal.
A method for additively manufacturing a microstructure from a caloric material includes providing a geometry of the microstructure to a processor of an additive manufacturing device, the geometry defining a plurality of microfeatures of the microstructure. The method also includes generating, via the processor, a three-dimensional (3D) model representative of the geometry of the microstructure, wherein one or more of the plurality of microfeatures are represented in the 3D model by a non-arcuate profile. Further, the method includes printing, via the additive manufacturing device, the microstructure from the caloric material according to the 3D model. As such, the non-arcuate profile reduces a file size of the 3D model as compared to an arcuate profile.
A system comprising a nonlinear medium (NLM), an optical transduction module, a dual homodyne detector and a processor is provided. The NLM receives at least a pump beam and issues the pump, probe and conjugate beams, where the beams are linearly polarized. Optics route the probe, the conjugate or both beams to the sample. The sample imparts polarization rotation to light that interacts therewith. The optical transduction module imparts to the interacted light an optical phase shift that is a 1:1 transduction of the polarization rotation, where at least one of the probe light or the conjugate light carries the imparted optical phase shift. The processor obtains the optical-phase shift based on respective detection signals from the dual homodyne detector and determines, based on the obtained optical-phase shift, at least one of a Faraday polarization rotation, a Kerr polarization rotation or a spin noise spectrum.