The Sparse Triangular Solver (SPTRSV) plays a critical role in solving structured grid problems. Yet, the commonly used sparse matrix storage formats for structured grid methods do not efficiently support SPTRSV in utilizing the instruction parallelism ...

The widespread use of vector graphics creates a significant demand for vectorization methods. While recent learning-based techniques have shown their capability to create vector images of clear topology, filling these primitives with gradients ...

Automatically extracting the geometric content from the hundreds of thousands of diagrams drawn in historical manuscripts would enable historians to study the diffusion of astronomical knowledge on a global scale. However, state-of-the-art ...

Stencil is a common computation pattern in high-performance computing (HPC) applications. While extensive work has been proposed to optimize stencil kernels on CPUs and GPUs, there is no consensus on how to best optimize stencils on multi-core Digital ...

The sparse matrix-vector multiplication (SpMV) is widely used in large-scale scientific computing and engineering. However, optimizing SpMV for high-performance digital signal processors (DSPs) has received limited attention. We present HaLAV, a method ...

In this paper, we introduce a new vectorized MATLAB-based algorithm for efficient serial computation of global matrix/force arising from finite element method (FEM) for meshes of any type and approximation order in linear elasticity. Because for-...${}_{\mathrm{}}$${}_{\mathrm{}}$

We conduct a systematic examination of vector arithmetic for polynomial multiplications in software. Vector instruction sets and extensions typically specify a fixed number of registers, each holding a power-of-two number of bits, and support a ...

The aim of this paper is to show that Goertzel and Reinsch algorithms for computing trigonometric sums can be efficiently vectorized using Intel AVX-512 intrinsics in order to utilize SIMD extensions of modern processors. Numerical experiments ...

Today, due to the unparalleled growth of multimedia data sharing, especially digital images, between users over insecure channels in real-time applications, cryptography algorithms have gained increasing attention for the secure and efficient ...

With the rapid adoption of deep learning workloads to resource-constrained edge devices, efficient and data-parallel computing paradigms are becoming increasingly important. The RISC-V ISA provides a set of vector extensions featuring powerful data ...

The construction of high-precision machine learning models relies on large-scale data collection from IoT devices. Since the training data involves sensitive user information, it is essential to design a privacy-preserving machine learning (PPML) ...

In this paper, we explore the cost of vectorization for multiplying polynomials with coefficients in ${\mathbb{Z}}_q$ for an odd prime q, as exemplified by NTRU Prime, a postquantum cryptosystem that found early adoption due to its inclusion in OpenSSH.

If there ...$\mathrm{}$${\mathbb{}}_{}$$\mathrm{}$

The lattice-based post-quantum cryptosystem NTRU is used by Google for protecting Google’s internal communication. In NTRU, polynomial multiplication is one of bottleneck. In this paper, we explore the interactions between polynomial ...$$$〈{}^{}〉$

The power supply system, as an indispensable electronic hardware module in most vehicles, needs the highest level of security and reliability to ensure the normal operation of the vehicle. Efficiently identifying any faulty battery at ...

• Proposing a new time series feature extraction and transformation method PVT.
• ...

We propose a new color image vectorization method converting raster images to resolution-independent scalable vector graphics. Starting from a quantized raster image, the method builds a hierarchical structure to represent its discontinuity set. ...

Sparse tensors are rapidly becoming critical components of modern deep learning workloads. However, developing high-performance sparse operators can be difficult and tedious, and existing vendor libraries cannot satisfy the escalating demands from new ...

Fully Homomorphic Encryption (FHE) is a scheme that allows a computational circuit to operate on encrypted data and produce a result that, when decrypted, yields the result of the unencrypted computation. While FHE enables privacy-preserving ...

Achieving peak throughput on modern CPUs requires maximizing the use of single-instruction, multiple-data (SIMD) or vector compute units. Single-program, multiple-data (SPMD) programming models are an effective way to use high-level programming languages ...

Numerical simulation often resorts to iterative in-place stencils such as the Gauss-Seidel or Successive Overrelaxation (SOR) methods. Writing high performance implementations of such stencils requires significant effort and time; it also involves non-...

Time series analysis is an important research topic and a key step in monitoring and predicting events in many fields. Recently, the Matrix Profile method, and particularly two of its Euclidean-distance-based implementations—SCRIMP and SCAMP—have ...$$