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In recent years, interest in RISC-V computing architectures has moved from academic to mainstream, especially in the field of High Performance Computing where energy limitations are increasingly a concern. As of this year, the first single board RISC-V ...

Language equivalence is closely related to Rocco De Nicola’s contributions to concurrency theory. Here we study language equivalence and state reduction for Nondeterministic Finite Automata (NFAs). We work in a linear algebraic setting based on ...

We introduce an algorithm for sketch vectorization with state-of-the-art accuracy and capable of handling complex sketches. We approach sketch vectorization as a surface extraction task from an unsigned distance field, which is implemented using a two-...

Maximizing cart value by increasing the number of items in electronic carts is one of the key strategies adopted by e-commerce platforms for optimal conversion of positive user intent during an online shopping session. Recommender systems play a key-role ...

Embedded applications extract the best power-performance trade-off from digital signal processors (DSPs) by making extensive use of vectorized execution. Rather than handwriting the many customized kernels these applications use, DSP engineers rely on ...

Polynomial interpolation is an important component of many computational problems. In several of these computational problems, failure to preserve positivity when using polynomials to approximate or map data values between meshes can lead to negative ...

Processing large-scale graphs with billions to trillions of edges requires efficiently utilizing parallel systems. However, current graph processing engines do not scale well beyond a few tens of computing nodes because they are oblivious to the ...

Macroscopic traffic flow models are powerful tools for assessing the level of service on freeway segments. One popular model is the Cell Transmission Model (CTM) proposed by Daganzo. The US highway capacity manual (HCM) operationalizes this model ...

In this new era where multiple GPU vendors are leading the supercomputing landscape, and multiple programming models are available to users, the drive to achieve performance portability across platforms faces new challenges. Consider stencil algorithms, ...

This paper proposes the Tensor Marshaling Unit (TMU), a near-core programmable dataflow engine for multicore architectures that accelerates tensor traversals and merging, the most critical operations of sparse tensor workloads running on today’s ...

As mobile networks and APPs are developed, user-generated content (UGC), which includes multi-source heterogeneous data like user reviews, tags, scores, images, and videos, has become an essential basis for improving the quality of personalized services. ...

A key advantage of vector graphics over raster graphics is their editability. For example, linear gradients define a spatially varying color fill with a few intuitive parameters, which are ubiquitously supported in standard vector graphics formats and ...

The study of numerical models for the human body has become a major focus of the research community in biology and medicine. For instance, numerical ionic models of a complex organ, such as the heart, must be able to represent individual cells and their ...

Nowadays, people share their opinions through social media. This information may be informative or non-informative. Filtering informative information from social media plays a challenging issue. Nevertheless, people will interact more with that ...

The compact data structures and irregular computation patterns in sparse matrix computations introduce challenges to vectorizing these codes. Available approaches primarily vectorize strided computation regions of a sparse code. In this work, we propose ...

Sparse computations, such as sparse matrix-dense vector multiplication, are notoriously hard to optimize due to their irregularity and memory-boundedness. Solutions to improve the performance of sparse computations have been proposed, ranging from ...

SIMD (Single Instruction Multiple Data) instructions apply the same operation to multiple elements simultaneously. Compilers transform codes to exploit SIMD instructions through auto-vectorization. Control flow can lead to challenges for auto-...

Decision tree ensembles are among the most commonly used machine learning models. These models are used in a wide range of applications and are deployed at scale. Decision tree ensemble inference is usually performed with libraries such as XGBoost, ...

Convolutional Neural Networks (CNNs) are widely used in real world applications, e.g, computer vision. Winograd based convolution is usually applied due to its low computation complexity. For the underling hardware, ARM many-core CPUs, by their price ...

This paper proposes software-defined floating-point number formats for graph processing workloads, which can improve performance in irregular workloads by reducing cache misses. Efficient arithmetic on software-defined number formats is challenging, ...