Heterogeneous many-cores are now an integral part of modern computing systems ranging
from embedding systems to supercomputers. While heterogeneous many-core design offers …

GPUs are widely used to accelerate the training of machine learning workloads. As modern
machine learning models become increasingly larger, they require a longer time to train …

General Matrix Multiplication (GEMM) is a key subroutine in highperformance computing.
While the mainstream linear algebra libraries can deliver high performance on large and …

Deep learning is emerging as a promising technique for building predictive models to
support code-related tasks like performance optimization and code vulnerability detection …

With the slowing of Moore's law and decline of Dennard scaling, computing systems
increasingly rely on specialized hardware accelerators in addition to general-purpose …

Graph neural networks (GNNs) are emerging as a powerful technique for modeling graph
structures. Due to the sparsity of real-world graph data, GNN performance is limited by …

Power and energy is the first-class design constraint for multi-core processors and is a
limiting factor for future-generation supercomputers. While modern processor design …

Map** queries to optimal computing devices at the operator-level presents a significant
challenge in stream processing systems (SPS) with heterogeneous computing resources …

The heterogeneous many-core architecture plays an important role in the fields of high-
performance computing and scientific computing. It uses accelerator cores with on-chip …

JavaScript (JS) is one of the most used programming languages for mobile applications. As
JS is increasingly used in computation-intensive and latency-sensitive components, JS …