Existing GPU lossy compressors suffer from expensive data movement overheads, inefficient memory access patterns, and high synchronization latency, resulting in limited throughput. This work proposes cuSZp2, a generic single-kernel error-bounded lossy ...

This work presents Agile Queue, a queue specifically designed to support high concurrency on modern GPUs. At its core is to replace conflicting accesses to shared objects with independent accesses to private data. The proposed Agile queue operates on ...

Satisfiability Modulo Theories on arithmetic theories have significant applications in many important domains. Previous efforts have been mainly devoted to improving the techniques and heuristics in sequential SMT solvers. With the development of ...

High-precision static analysis can effectively detect Null Pointer Dereference (NPD) vulnerabilities in C language, but the performance overhead is significant. In recent years, researchers have attempted to enhance the efficiency of static analysis by ...

Deep Neural Networks (DNNs) have gained widespread popularity in different domain applications due to their dominant performance. Despite the prevalence of massively parallel multi-core processor architectures, adopting large DNN models in ...

• Parallel Computing.
• Machine Learning.
• DNN Model Partitioning.
• Embedded Systems.
• Embedded Software.

In recent years, Artificial Intelligence (AI) has reshaped various facets of our day-to-day lives. To evaluate both hardware and software deployment of Machine Learning (ML) applications, it is necessary to measure system-wide performance and accuracy. ...

As production scene complexity and CPU core count increase, the performance of software used to interact with the scenes may not scale accordingly. Filament is Animal Logic’s in-house, USD-based, PyQt lighting DCC, and a key area for improving Filament ...

It is known that, in spite of a large number of methods for numerical solutions to various classes of problems, the choice of the most efficient method for solving a particular problem under specific values of its parameters requires a large ...

In this paper, we propose a new library for storing arrays in a distributed fashion on distributed memory systems. From a programmer's perspective, these arrays behave for arbitrary reads as if they were allocated in shared memory. When it comes to ...

The shortest paths problem is a fundamental challenge in graph theory, with a broad range of potential applications. The algorithms based on matrix multiplication exhibits excellent parallelism and scalability, but is constrained by high memory ...

This paper introduces a novel K-means clustering algorithm, an advancement on the conventional Big-means methodology. The proposed method efficiently integrates parallel processing, stochastic sampling, and competitive optimization to create a ...

Recently, machine learning has established itself as a valuable tool for researchers to analyze their data and draw conclusions in various scientific fields, such as High Energy Physics (HEP). Commonly used machine learning libraries, such as Keras and ...

Predicting future traffic conditions from urban sensor data is crucial for smart city applications. Recent traffic forecasting methods are derived from Spatio-Temporal Graph Convolution Networks (STGCNs). Despite their remarkable achievements, these ...

Reconfigurable architecture has great potential in computation-intensive and memory-intensive applications due to its flexible information configuration. Aiming at the problem of low computing efficiency caused by the inconsistency between ...

In light of previous endeavors and trends in the realm of parallel programming, HPPython emerges as an essential superset that enhances the accessibility of parallel programming for developers, facilitating scalability across multiple nodes. Despite ...

Approximate nearest neighbor (ANN) search in high dimensional Euclidean space is a fundamental problem of big data processing. Locality-Sensitive Hashing (LSH) is a popular scheme to solve the ANN search problem. In the index phase, an LSH scheme ...

In modern HPC systems with deep hierarchical architectures, large-scale applications often struggle to efficiently utilize the abundant cores due to the saturation of resources such as memory. Co-allocating multiple applications to share compute ...

The demand for large-scale computing, such as the application of AI in big data analytics and engineering simulations, has been steadily increasing. Meanwhile, many companies and individuals now own smartphones and PCs, but a significant portion ...

The parameters of deep learning (DL) have ballooned from millions to trillions over the past decade, thus cannot be fully placed in a limited GPU memory. Existing works offload the parameter-update stage of training DL model to CPU, thus leveraging CPU ...

The Dataflow for C++(DFCPP) designed and implemented in this paper is a parallel programming library for dataflow computing on a general control flow hardware platform. Compared with existing dataflow programming libraries, DFCPP has an easy-to-use user ...