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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Enabling Fortran Standard Parallelism in GAMESS for Accelerated Quantum Chemistry Calculations

The performance of Fortran 2008 DO CONCURRENT (DC) relative to OpenACC and OpenMP target offloading (OTO) with different compilers is studied for the GAMESS quantum chemistry application. Specifically, DC and OTO are used to offload the Fock build, which is a computational bottleneck in most quantum chemistry codes, to GPUs. The DC Fock build performance is studied on NVIDIA A100 and V100 accelerators and compared with the OTO versions compiled by the NVIDIA HPC, IBM XL, and Cray Fortran compilers. The results show that DC can speed up the Fock build by 3.0× compared with that of the OTO model. Finally, with similar offloading efforts, DC is a compelling programming model for offloading Fortran applications to GPUs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Quantum logic gate synthesis as a Markov decision process

Reinforcement learning has witnessed recent applications to a variety of tasks in quantum programming. The underlying assumption is that those tasks could be modeled as Markov decision processes (MDPs). Here, we investigate the feasibility of this assumption by exploring its consequences for single-qubit quantum state preparation and gate compilation. By forming discrete MDPs, we solve for the optimal policy exactly through policy iteration. We find optimal paths that correspond to the shortest possible sequence of gates to prepare a state or compile a gate, up to some target accuracy. Our method works in both the absence and presence of noise and compares favorably to other quantum compilation methods, such as the Ross–Selinger algorithm. This work provides theoretical insight into why reinforcement learning may be successfully used to find optimally short gate sequences in quantum programming.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Evaluating Performance Portability with the CMS Heterogeneous Pixel Reconstruction code

In the past years the landscape of tools for expressing parallel algorithms in a portable way across various compute accelerators has continued to evolve significantly. There are many technologies on the market that provide portability between CPU, GPUs from several vendors, and in some cases even FPGAs. These technologies include C++ libraries such as Alpaka and Kokkos, compiler directives such as OpenMP, the SYCL open specification that can be implemented as a library or in a compiler, and standard C++ where the compiler is solely responsible for the offloading. Given this developing landscape, users have to choose the technology that best fits their applications and constraints. For example, in the CMS experiment the experience so far in heterogeneous reconstruction algorithms suggests that the full application contains a large number of relatively short computational kernels and memory transfer operations. In this work we use a stand-alone version of the CMS heterogeneous pixel reconstruction code as a realistic use case of HEP reconstruction software that is capable of leveraging GPUs effectively. We summarize the experience of porting this code base from CUDA to Alpaka, Kokkos, SYCL, std::par, and OpenMP offloading. We compare the event processing throughput achieved by each version on NVIDIA and AMD GPUs as well as on a CPU, and compare those to what a native version of the code achieves on each platform.

Andriotis, Nikolaos↗
DOE OSTI2021

Cosmological constraints from H ii starburst galaxy, quasar angular size, and other measurements

ABSTRACT We compare the constraints from two (2019 and 2021) compilations of H ii starburst galaxy (H iiG) data and test the model independence of quasar (QSO) angular size data using six spatially flat and non-flat cosmological models. We find that the new 2021 compilation of H iiG data generally provides tighter constraints and prefers lower values of cosmological parameters than those from the 2019 H iiG data. QSO data by themselves give relatively model-independent constraints on the characteristic linear size, lm, of the QSOs within the sample. We also use Hubble parameter [H(z)], baryon acoustic oscillation (BAO), Pantheon Type Ia supernova (SN Ia) apparent magnitude (SN-Pantheon), and DES-3 yr binned SN Ia apparent magnitude (SN-DES) measurements to perform joint analyses with H iiG and QSO angular size data, since their constraints are not mutually inconsistent within the six cosmological models we study. A joint analysis of H(z), BAO, SN-Pantheon, SN-DES, QSO, and the newest compilation of H iiG data provides almost model-independent summary estimates of the Hubble constant, $H_0=69.7\pm 1.2\ \rm {km\,s^{-1}\,Mpc^{-1}}$, the non-relativistic matter density parameter, $\Omega _{\rm m_0}=0.293\pm 0.021$, and lm = 10.93 ± 0.25 pc.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Do quasar X-ray and UV flux measurements provide a useful test of cosmological models?

ABSTRACT The recent compilation of quasar (QSO) X-ray and ultraviolet (UV) flux measurements include QSOs that appear to not be standardizable via the X-ray luminosity and UV luminosity (LX–LUV) relation and so should not be used to constrain cosmological model parameters. Here, we show that the largest of seven sub-samples in this compilation, the SDSS-4XMM QSOs that contribute about 2/3 of the total QSOs, have LX–LUV relations that depend on the cosmological model assumed and also on redshift, and is the main cause of the similar problem discovered earlier for the full QSO compilation. The second and third biggest sub-samples, the SDSS-Chandra and XXL QSOs that together contribute about 30 per cent of the total QSOs, appear standardizable, but provide only weak constraints on cosmological parameters that are not inconsistent with the standard spatially flat ΛCDM model or with constraints from better-established cosmological probes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Gamma-ray burst data strongly favour the three-parameter fundamental plane (Dainotti) correlation over the two-parameter one

ABSTRACT Gamma-ray bursts (GRBs), observed to redshift z = 9.4, are potential probes of the largely unexplored z ∼ 2.7–9.4 part of the early Universe. Thus, finding relevant relations among GRB physical properties is crucial. We find that the Platinum GRB data compilation, with 50 long GRBs (with relatively flat plateaus and no flares) in the redshift range 0.553 ≤ z ≤ 5.0, and the LGRB95 data compilation, with 95 long GRBs in 0.297 ≤ z ≤ 9.4, as well as the 145 GRB combination of the two, strongly favour the 3D Fundamental Plane (Dainotti) correlation (between the peak prompt luminosity, the luminosity at the end of the plateau emission, and its rest-frame duration) over the 2D one (between the luminosity at the end of the plateau emission and its duration). The 3D Dainotti correlations in the three data sets are standardizable. We find that while LGRB95 data have ∼50 per cent larger intrinsic scatter parameter values than the better-quality Platinum data, they provide somewhat tighter constraints on cosmological-model and GRB-correlation parameters, perhaps solely due to the larger number of data points, 95 versus 50. This suggests that when compiling GRB data for the purpose of constraining cosmological parameters, given the quality of current GRB data, intrinsic scatter parameter reduction must be balanced against reduced sample size.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Simulating non-native cubic interactions on noisy quantum machines

As a milestone for general-purpose computing machines, we demonstrate that quantum processors can be programed to efficiently simulate dynamics that are not native to the hardware. Moreover, on noisy devices without error correction, we show that simulation results are significantly improved when the quantum program is compiled using modular gates instead of a restricted set of standard gates. We demonstrate the general methodology by solving a cubic interaction problem, which appears in nonlinear optics, gauge theories, as well as plasma and fluid dynamics. To encode the non-native Hamiltonian evolution, we decompose the Hilbert space into a direct sum of invariant subspaces in which the nonlinear problem is mapped to a finite-dimensional Hamiltonian simulation problem. Furthermore, in a three-states example, the resultant unitary evolution is realized by a product of approximately 20 standard gates, using which approximately ten simulation steps can be carried out on state-of-the-art quantum hardware before results are corrupted by decoherence. In comparison, the simulation depth is improved by more than an order of magnitude when the unitary evolution is realized as a single cubic gate, which is compiled directly using optimal control. Alternatively, parametric gates may also be compiled by interpolating control pulses. Modular gates thus obtained provide high-fidelity building blocks for quantum Hamiltonian simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

On Using Linux Kernel Huge Pages with FLASH, an Astrophysical Simulation Code

We present efforts at improving the performance of FLASH, a multi-scale, multi-physics simulation code principally for astrophysical applications, by using huge pages on Ookami, an HPE Apollo 80 A64FX platform. FLASH is written principally in modern Fortran and makes use of the PARAMESH library to manage a block-structured adaptive mesh. We explored options for enabling the use of huge pages with several compilers, but we were only able to successfully use huge pages when compiling with the Fujitsu compiler. As a result, the use of huge pages substantially reduced the number of translation lookaside buffer misses, but overall performance gains were marginal.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A Pulse Generation Framework with Augmented Program-aware Basis Gates and Criticality Analysis

Near-term intermediate scale quantum (NISQ) de- vices are subject to considerable noise and short coherence time. Consequently, it is critical to minimize circuit execution latency. Traditionally, each basis gate of a transpiled circuit is decoded into a fixed episode of the device control pulses. Recently, people started to investigate merged pulse generation for customized gates through quantum optimal control (QOC). However, existing QOC approaches face the challenges of (i) restricted search space due to prohibitive compilation overhead; (ii) suboptimal end-to-end performance due to aggressive local optimization and falsely introduced dependency among the customized gates; (iii) inadequate adaptivity towards system calibration, which is critical for NISQ devices. In this work, we propose PAQOC, a novel QOC framework that can (i) automatically detect frequently encountered gate patterns in the logical circuit by modeling the problem as a subgraph mining process and reuse these patterns to enable much larger search space exploration (i.e., program aware); (ii) systemically construct customized gate-set based on the impact to the overall program latency (i.e., criticality-aware); and (iii) quickly adapt to system re-calibration thanks to the small-scale pattern-based gate generation (i.e., adaptivity-aware). PAQOC achieves a good tradeoff between circuit performance and compilation time, allowing fully automatic, single stop, ad- hoc customized pulse generation for more efficient execution of user programs on NISQ devices. Evaluations using fifteen applications show that PAQOC can achieve on average 1.95× speedup of the circuit latency and achieve on average 36.7% reduction in compilation overhead. With PAQOC, circuits can run faster with reduced noise, allowing deeper circuits to be tested within the coherence time of present NISQ platforms.

Chen, Yanhao↗
DOE OSTI2021

Reoptimization of Quantum Circuits via Hierarchical Synthesis

The current phase of quantum computing is in the Noisy Intermediate-Scale Quantum (NISQ) era. On NISQ devices, two-qubit gates such as CNOTs are much noisier than single-qubit gates, so it is essential to minimize their count. Quantum circuit synthesis is a process of decomposing an arbitrary unitary into a sequence of quantum gates, and can be used as an optimization tool to produce shorter circuits to improve overall circuit fidelity. However, the time-to-solution of synthesis grows exponentially with the number of qubits. As a result, synthesis is intractable for circuits on a large qubit scale. In this paper, we propose a hierarchical, block-by-block opti-mization framework, QGo, for quantum circuit optimization. Our approach allows an exponential cost optimization to scale to large circuits. QGo uses a combination of partitioning and synthesis: 1) partition the circuit into a sequence of independent circuit blocks; 2) re-generate and optimize each block using quantum synthesis; and 3) re-compose the final circuit by stitching all the blocks together. We perform our analysis and show the fidelity improvements in three different regimes: small-size circuits on real devices, medium-size circuits on noisy simulations, and large-size circuits on analytical models. Our technique can be applied after existing optimizations to achieve higher circuit fidelity. Further, using a set of NISQ benchmarks, we show that QGo can reduce the number of CNOT gates by 29.9% on average and up to 50% when compared with industrial compiler optimizations such as t|ket). When executed on the IBM Athens system, shorter depth leads to higher circuit fidelity. We also demonstrate the scalability of our QGo technique to optimize circuits of 60+ qubits, Our technique is the first demonstration of successfully employing and scaling synthesis in the compilation tool chain for large circuits. Overall, our approach is robust for direct incorporation in production compiler toolchains to further improve the circuit fidelity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

PowerMappeR: Power-Optimized Mapping of SNNs onto ReRAM Crossbars coupled via Packet-Switched NoCs

Many recent efforts in developing hardware-accelerated spiking neural networks (SNNs) are characterized by deep co-design between algorithms, architectures, and devices. Architectural advances overcome device constraints by coupling together many small resistive-RAM (ReRAM) crossbars via a network-on-chip (NoC) for neuromorphic component operation. Concurrently, improved SNN training methods increase accuracy and structural sparsity in networks despite growing problem sizes. Finally, compilers leverage these attributes to minimize area and inter-crossbar communication while mapping large SNNs to sophisticated architectures. However, for compiler-driven co-design to realize increasingly complex and profitable optimizations, a compile-time view of power consumption is critical. We present PowerMappeR to express and optimize over mapping-, architecture-, and device-specific power consumption information. By modeling the dynamic power of well-established components, we develop an integer linear programming (ILP)-based, encoding-agnostic, parametric power estimation model. Using this model, we demonstrate practical improvements in area and inter-crossbar communication by 0%–9.5% and 1.4%–5.1%, respectively. We also limit hotspot formation during optimization, achieving comparable or better results in targeted metrics with up to 96.4%–97.1% restriction of hotspot magnitude. Finally, we introduce profile-guided formulations to reduce worst-case and expected-case hotspot magnitude by 40.7%–69.5% and 40.6%–56.3%, respectively. Optimizing worst-case hotspot magnitude incidentally improves expected-case magnitude by 10.85%–33.45%. Reciprocally, optimizing expected-case magnitude incidentally improves worst-case magnitude by 4.33%–39.87%. Validation against hardware simulators confirms that PowerMappeR can decrease dynamic power consumption by 12.6%–27.3%.

Pohl, Devin [ORNL] (ORCID:0009000040149027)↗
DOE OSTI2022

Let Each Quantum Bit Choose Its Basis Gates

Near-term quantum computers are primarily limited by errors in quantum operations (or gates) between two quantum bits (or qubits). A physical machine typically provides a set of basis gates that include primitive 2-qubit (2Q) and 1-qubit (1Q) gates that can be implemented in a given technology. 2Q entangling gates, coupled with some 1Q gates, allow for universal quantum computation. In superconducting technologies, the current state of the art is to implement the same 2Q gate between every pair of qubits (typically an XX-or XY-type gate). This strict hardware uniformity requirement for 2Q gates in a large quantum computer has made scaling up a time and resource-intensive endeavor in the lab. We propose a radical idea – allow the 2Q basis gate(s) to differ between every pair of qubits, selecting the best entangling gates that can be calibrated between given pairs of qubits. This work aims to give quantum scientists the ability to run meaningful algorithms with qubit systems that are not perfectly uniform. Scientists will also be able to use a much broader variety of novel 2Q gates for quantum computing. We develop a theoretical framework for identifying good 2Q basis gates on “nonstandard” Cartan trajectories that deviate from “standard” trajectories like XX. We then introduce practical methods for calibration and compilation with nonstandard 2Q gates, and discuss possible ways to improve the compilation. To demonstrate our methods in a case study, we simulated both standard XY-type trajectories and faster, nonstandard trajectories using an entangling gate architecture with far-detuned transmon qubits. We identify efficient 2Q basis gates on these nonstandard trajectories and use them to compile a number of standard benchmark circuits such as QFT and QAOA. Furthermore, our results demonstrate an 8x improvement over the baseline 2Q gates with respect to speed and coherence-limited gate fidelity.

quantum computing↗
DOE OSTI2020

Extending XACC for Quantum Optimal Control

Quantum computing vendors are beginning to open up application programming interfaces for direct pulse-level quantum control. With this, programmers can begin to describe quantum kernels of execution via sequences of arbitrary pulse shapes. This opens new avenues of research and development with regards to smart quantum compilation routines that enable direct translation of higher-level digital assembly representations to these native pulse instructions. In this work, we present an extension to the XACC system-level quantum-classical software framework that directly enables this compilation lowering phase via user-specified quantum optimal control techniques. This extension enables the translation of digital quantum circuit representations to equivalent pulse sequences that are optimal with respect to the backend system dynamics. Our work is modular and extensible, enabling third party optimal control techniques and strategies in both C++ and Python. We demonstrate this extension with familiar gradient-based methods like gradient ascent pulse engineering (GRAPE), gradient optimization of analytic controls (GOAT), and Krotov's method. Our work serves as a foundational component of future quantum-classical compiler designs that lower high-level programmatic representations to low-level machine instructions.

Nguyen, Thien↗
DOE OSTI2021

A MLIR Dialect for Quantum Assembly Languages

We demonstrate the utility of the Multi-Level Intermediate Representation (MLIR) for quantum computing. Specifically, we extend MLIR with a new quantum dialect that enables the expression and compilation of common quantum assembly languages. The true utility of this dialect is in its ability to be lowered to the LLVM intermediate representation (IR) in a manner that is adherent to the quantum intermediate representation (QIR) specification recently proposed by Microsoft. We leverage a qcor-enabled implementation of the QIR quantum runtime API to enable a retargetable (quantum hardware agnostic) compiler workflow mapping quantum languages to hybrid quantum-classical binary executables and object code. We evaluate and demonstrate this novel compiler workflow with quantum programs written in OpenQASM 2.0. We provide concrete examples detailing the generation of MLIR from OpenQASM source files, the lowering process from MLIR to LLVM IR, and ultimately the generation of executable binaries targeting available quantum processors.

Mccaskey, Alex↗
DOE OSTI2022

KokkACC: Enhancing Kokkos with OpenACC

Template metaprogramming is gaining popularity as a high-level solution for achieving performance portability on heterogeneous computing resources. Kokkos is a representative approach that offers programmers high-level abstractions for generic programming while most of the device-specific code generation and optimizations are delegated to the compiler through template specializations. For this, Kokkos provides a set of device-specific code specializations in multiple back ends, such as CUDA and HIP. Unlike CUDA or HIP, OpenACC is a high-level and directive-based programming model. This descriptive model allows developers to insert hints (pragmas) into their code that help the compiler to parallelize the code. The compiler is responsible for the transformation of the code, which is completely transparent to the programmer. This paper presents an OpenACC back end for Kokkos: KokkACC. As an alternative to Kokkos’s existing device-specific back ends, KokkACC is a multi-architecture back end providing a high-productivity programming environment enabled by OpenACC’s high-level and descriptive programming model. Moreover, we have observed competitive performance; in some cases, KokkACC is faster (up to 9×) than NVIDIA’s CUDA back end and much faster than OpenMP’s GPU offloading back end. This work also includes implementation details and a detailed performance study conducted with a set of mini-benchmarks (AXPY and DOT product) and three mini-apps (LULESH, miniFE and SNAP, a LAMMPS proxy mini-app).

Valero Lara, Pedro↗
DOE OSTI2024

Jackson, L., Johnson, M.B., Latrach, A., Grimes, D., Martinez, C., and Mclaughlin, J.F., 2024, Multidisciplinary geotechnical data collection, curation, and analysis for conformity with the regulatory framework for geologic carbon storage in Wyoming, USA: Geological Society of America Abstracts with Programs. Vol. 56, No. 5, 2024, doi: 10.1130/abs/2024AM-405024

Title: Multidisciplinary Geotechnical Data Collection, Curation, and Analysis for Conformity with the Regulatory Framework for Geologic Carbon Storage in Wyoming, USA. Text: Construction and operation of wells for geologic sequestration of carbon dioxide necessitate that they are permitted under the Environmental Protection Agency’s Underground Injection Control Class VI requirements. Class VI wells conform to stringent requirements to ensure long-term safety and integrity of the storage site and the protection of Underground Sources of Drinking Water. Entities pursuing Class VI permitting must provide comprehensive geologic site characterization, including regional geologic structure and stratigraphy, aquifer information, reservoir and confining unit geomechanical properties, geochemical analyses, assessment of trapping capacity and mechanisms, and a variety of other of multidisciplinary geotechnical data. The Wyoming Class VI Site Characterization Database Project is focused on developing a geologic site characterization database of geotechnical information, which has been compiled and verified from established, public databases/entities and scientific literature to expedite Class VI permitting in Sweetwater County within the Greater Green River Basin of southern Wyoming. The preliminary suite of compiled data from 14,000 wells includes 8,000 wells with logs and 7,250 wells with formation tops, ~70 wells with core data (e.g., X-Ray diffraction, petrographic, and petrophysical data), ~2,500 water analyses, ~740 seismic events data, and ~520 bottom-hole temperature measurements. Future work on—and stemming from—this project will include new core analyses, calculation and interpolation of subsurface temperature gradients, mechanical earth models, geochemical simulations, storage capacity estimation, stratigraphic column generation and correlation, and construction of subsurface maps. Finally, this work will help to inspire and facilitate subsurface data compilation and curation beyond Sweetwater County, Wyoming.

42 ENGINEERING↗
DOE OSTI2022

Experiences with Porting the FLASH Code to Ookami, an HPE Apollo 80 A64FX Platform

We present initial experiences with running the community simulation code FLASH, developed at the University of Chicago for multi-scale multi-physics applications, on Ookami, a technology testbed featuring the A64FX processor developed by Fujitsu. Our effort focused largely on running FLASH “right out of the box” to see which combinations of compilers and software implementations (e.g. MPI) allowed the code to run with minimal modification. FLASH was one application in a larger effort to deploy Ookami; it served as a test for different versions of newly installed software, and as a cornerstone for the FAQ page of the Ookami website. Here, we report on our results with different compilers and other software, along with our initial scaling results and attempts to utilize the A64FX’s SVE instructions and NUMA architecture. We found that FLASH readily ran with different compilers and MPI implementations, and showed the expected good scaling with no turning. However, more work must be done to fully take advantage of the A64FX’s architectural features and produce a significant speedup for FLASH on Ookami.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Studying CPU and memory utilization of applications on Fujitsu A64FX and Nvidia Grace Superchip

ARM-based manycore CPU architectures are well-positioned to provide the rising memory throughput requirements of modern data intensive scientific applications in High Performance Computing (HPC). The Fujitsu A64FX CPU platform is based on the ARM v8.2A architecture, and is the processor of the flagship Japanese supercomputer - "Fugaku", which was previously ranked as the #1 supercomputer in the world according to the Top500 list. The Nvidia Grace superchip features 144 Neoverse V2 cores based on the ARMv9 architecture with 4x128b SVE2, providing exceptional computational power. The chip supports up to 480GB of memory, making it ideal for AI, machine learning, and scientific computing workloads. In this paper, we conduct a thorough performance exploration of a variety of parallel bandwidth-sensitive benchmarks and applications compiled with the native Fujitsu compiler on a Fugaku A64FX compute node and ARM (LLVM) Compiler on an NVIDIA Grace superchip compute node, engaging all the computational cores per cluster using OpenMP multithreading (assuming the cores can drive the available bandwidth). Our ultimate goals are to study the resource utilization of scientific applications and benchmarks on A64FX and Grace superchip, considering graph application scenarios ( GAP Benchmark suite) and eleven appli- cation proxies from the Rodinia heterogeneous benchmark suite (considering domains such as Data Mining, Bioinformatics, Fluid Dynamics, Pattern Recognition, etc.). Through exhaustive performance monitoring, we quantify the resource utilization of diverse OpenMP-based HPC applications on both the Fujitsu A64FX and the Nvidia Grace Superchip platforms.

benchmarking, Performance Analysis, High performan↗