Deterministic concurrency control (DCC) guarantees that the same input transactions produce the same serializable result. It offers benefits in both distributed databases and blockchain systems. Dozens of DCC algorithms have emerged in the past decade. ...

The GIST descriptor is a classic feature descriptor primarily used for scene categorization and recognition tasks. It drives a bank of Gabor filters, which respond to edges and textures at various scales and orientations to capture the spatial structures ...

Graph Neural Networks (GNNs) have achieved remarkable successes in various graph-based learning tasks, thanks to their ability to leverage advanced GPUs. However, GNNs currently face challenges arising from the concurrent use of advanced Tensor Cores (TCs)...

The Sparse General Matrix-Matrix multiplication (SpGEMM) is a fundamental component for many applications, such as algebraic multigrid methods (AMG), graphic processing, and deep learning. However, the unbearable latency of computing high-dimensional, ...

Owing to their remarkable representation capabilities for heterogeneous graph data, Heterogeneous Graph Neural Networks (HGNNs) have been widely adopted in many critical real-world domains such as recommendation systems and medical analysis. Prior to ...

In recent years, deploying deep learning models on edge devices has become pervasive, driven by the increasing demand for intelligent edge computing solutions across various industries. From industrial automation to intelligent surveillance and healthcare,...

With the explosive growth of graph data, distributed graph processing has become popular, and many graph hardware accelerators use distributed frameworks. Graph partitioning is foundation in distributed graph processing. However, dynamic changes in graph ...

The hash-based signature (HBS) is the most conservative and time-consuming among many post-quantum cryptography (PQC) algorithms. Two HBSs, LMS and XMSS, are the only PQC algorithms standardised by the National Institute of Standards and Technology (NIST) ...

Many real-world networks are characterized by being temporal and dynamic, wherein the temporal information signifies the changes in connections, such as the addition or removal of links between nodes. Employing random walks on these temporal networks is a ...

The edge to data center computing continuum is the aggregation of computing resources located anywhere between the network edge (e.g., close to 5G antennas), and servers in traditional data centers. Kubernetes is the de facto standard for the ...

Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequences. In pHMMs, sequences are represented as graph structures, where states ...

Leveraging the SIMD capability of modern CPU architectures is mandatory to take full advantage of their increased performance. To exploit this capability, binary executables must be vectorized, either manually by developers or automatically by a tool. For ...

Dense linear algebra operations appear very frequently in high-performance computing (HPC) applications, rendering their performance crucial to achieve optimal scalability. As many modern HPC clusters contain multi-GPU nodes, BLAS operations are ...

A typical compiler flow relies on a uni-directional sequence of translation/optimization steps that lower the program abstract representation, making it hard to preserve higher-level program information across each transformation step. On the other hand, ...

Memory efficiency is crucial in training deep learning networks on resource-restricted devices. During backpropagation, forward tensors are used to calculate gradients. Despite the option of keeping those dependencies in memory until they are reused in ...

Data movement is a common performance bottleneck, and its chief remedy is caching. Traditional cache management is transparent to the workload: data that should be kept in cache are determined by the recency information only, while the program information,...

We investigate the programming of reactive systems combining closed-loop control with performance-intensive components such as Machine Learning (ML). Reactive control systems are often safety-critical and associated with real-time execution requirements, ...

Sparse data applications have irregular access patterns that stymie modern memory architectures. Although hyper-sparse workloads have received considerable attention in the past, moderately-sparse workloads prevalent in machine learning applications, ...

Two novel algorithms for the all-gather operation resilient to imbalanced process arrival patterns (PATs) are presented. The first one, Background Disseminated Ring (BDR), is based on the regular parallel ring algorithm often supplied in MPI ...

Well-crafted libraries deliver much higher performance than code generated by sophisticated application programmers using advanced optimizing compilers. When a code pattern for which a well-tuned library implementation exists is found in the source code ...