Hash tables are a fundamental data structure for effectively storing and accessing sparse data, with widespread usage in domains ranging from computer graphics to machine learning. This study surveys the state-of-the-art research on data-parallel hashing techniques for emerging massively-parallel, many-core GPU architectures. This survey identifies key factors affecting the performance of different techniques and suggests directions for further research.

Data-Parallel Hashing Techniques for GPU Architectures

Hash tables are one of the most fundamental data structures for effectively storing and accessing sparse data, with widespread usage in domains ranging from computer graphics to machine learning. This study surveys the state-of-the-art research on data-parallel hashing techniques for emerging massively-parallel, many-core GPU architectures. Key factors affecting the performance of different hashing schemes are discovered and used to suggest best practices and pinpoint areas for further research.

The neutral atom array has gained prominence in quantum computing for its scalability and operation fidelity. Previous works focus on \textit{fixed} atom arrays (FAA) that necessitate extensive SWAP operations for long-range interactions. This work explores a novel architecture known as \textit{field programmable qubit array (FPQA)}, which uniquely allows for coherent atom movements during circuit execution and significantly \textit{reduces the cost of long-range interactions}. However, the atom movements have multiple hardware constraints, making movement scheduling very challenging. In this work, we introduce FPQA-C, a compilation framework tailored for qubit mapping, atom movement, and gate scheduling of FPQA. It contains a qubit-array mapper to decide the coarse-grained mapping of qubit to arrays, leveraging MAX k-Cut on a constructed gate frequency graph to minimize SWAP overhead. Subsequently, a qubit-atom mapper determines the fine-grained mapping of qubits to specific atoms in the array, and considers load balance to prevent hardware constraint violations. We further propose a high-parallelism router that iteratively identifies parallelizable 2Q gates and decide the atom movements and gate executions, thus improving the parallelism. Besides, for fault-tolerant computing with FPQA, we provide comprehensive simulations evaluating logical error rates, execution times, physical qubit requirements, code distances, and bandwidth. We rigorously assess FPQA-C across 20+ diverse benchmarks, including generic circuits (arbitrary, QASMBench, SupermarQ), Quantum Simulation, and QAOA circuits. FPQA-C consistently outperforms the IBM Superconducting, FAA with long-range gates, FAA with rectangular and triangular topologies, achieving 2Q gate reductions by factors of 5.3x, 3.2x, 3.4x, and 2.6x, and circuit depth reductions by factors of 3.6x, 3.2x, 3.1x, and 2.2x, respectively. 

FPQA-C: A Compilation Framework for Field Programmable Qubit Array

The machine learning has been widely used in intelligent data mining. The high-computational complexity of machine learning and huge data volume present challenges to computing platforms. Graphics processor unit (GPU) provides powerful computing support for machine learning but shows different performances under different computing scales and/or different development methods. Analysing the performance of GPUs in different application scenarios helps to improve computing performance. In this study, the matrix multiplication, which is a common and time-consuming computation operation in machine learning, is performed on different data scales and different development methods to analyse the relationship between GPU computing performance with matrix scale and development methods. The experimental data shows that the performance of GPU is not much improved compared with the central processing unit in small-scale data calculation. Also, using a high-level application programming interface for GPU development is less computing-efficient than the GPU programming language computes unified device architecture C.

GPU computing performance analysis on matrix multiplication

Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is applied to Machine Learning (ML) applications, it forms a Quantum Machine Learning (QML) system. After discussing the basic concepts of QC and its advantages over classical computing, this paper reviews the key aspects of QML in a comprehensive manner. We discuss different QML algorithms and their domain applicability, quantum datasets, hardware technologies, software tools, simulators, and applications. In this survey, we provide valuable information and resources for readers to jumpstart into the current state-of-the-art techniques in the QML field.

pdf/a-survey-on-quantum-machine-learning-current-trends-2eohfsb1r8.pdf

A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead

The neutral-atom quantum computer"Aquila"is QuEra's latest device available through the Braket cloud service on Amazon Web Services (AWS). Aquila is a"field-programmable qubit array"(FPQA) operated as an analog Hamiltonian simulator on a user-configurable architecture, executing programmable coherent quantum dynamics on up to 256 neutral-atom qubits. This whitepaper serves as an overview of Aquila and its capabilities: how it works under the hood, key performance benchmarks, and examples that demonstrate some quintessential applications. This includes an overview of neutral-atom quantum computing, as well as five examples of increasing complexity from single-qubit dynamics to combinatorial optimization, implemented on Aquila. This whitepaper is intended for readers who are interested in learning more about neutral-atom quantum computing, as a guide for those who are ready to start using Aquila, and as a reference point for its performance as an analog quantum computer.

Aquila: QuEra's 256-qubit neutral-atom quantum computer

The region of DNA immediately in front of a gene body (also called upstream region) contains short (8-20 base) sequence motifs that help to control when that gene is turned on and off. Unfortunately, these motifs are generally unknown and commonly degenerate. In this work, we propose a motif-finding framework that, given a set of gene upstream regions, performs their all-to-all pairwise comparison and identifies all the motifs of length k (k-mers) that are common to any pair of upstream regions or differ in at most d characters. Our framework stores the k-mers found in each gene in a multi-level hash table. Our hash table design optimizes hash table comparison (rather than hash table insertion or lookup), is highly parallelizable and easily maps onto GPU. We propose four GPU kernels for pairwise hash table comparison, each leveraging a distinct parallelization approach. We study how different factors (the hash function, the number of buckets and the settings of other implementation-specific parameters) affect the performance of our implementation. Experimental results performed using an average-size yeast genome show that our fastest GPU kernel outperforms an 8-thread, cache-efficient CPU implementation by a factor of ~52x.

/pdf/parallel-gene-upstream-comparison-via-multi-level-hash-38p9ypn65d.pdf

Parallel Gene Upstream Comparison via Multi-Level Hash Tables on GPU

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Aquila: QuEra's 256-qubit neutral-atom quantum computer

Parallel Gene Upstream Comparison via Multi-Level Hash Tables on GPU