WebOverall, GraphDynS achieves 4.4× speedup and 11.6× less energy on average with half the memory bandwidth compared to a state-of-the-art GPGPU-based solution. WebFeb 1, 2024 · This work proposes GraphDynS, a hardware/software co-design with decoupled datapath and data-aware dynamic scheduling that can elaborately schedule …
ScalaGraph: A Scalable Accelerator for Massively Parallel …
WebOct 12, 2024 · To achieve this goal, we propose GraphDynS, a hardware/software co-design with decoupled datapath and data-aware dynamic scheduling. Aware of data … WebDec 2, 2024 · An overview of Coupled Data 결합 데이터: Weakly Coupled Data, Strongly Coupled Data, dartmouth hospital gift shop
Graphdiyne: synthesis, properties, and applications
WebJun 1, 2024 · Graph pattern mining (GPM) is a class of algorithms widely used in many real-world applications in bio-medicine, e-commerce, security, social sciences, etc. GPM is a computationally intensive problem with an enormous amount of coarse-grain parallelism and therefore, attractive for hardware acceleration. Unfortunately, existing GPM accelerators ... WebThis paper proposes data-aware dynamic scheduling considering data dependencies, which elaborately schedules program on the fly, effectively tackling all three types of … WebMar 31, 2024 · Overall, GraphDynS achieves 4.4× speedup and 11.6× less energy on average with half the memory bandwidth compared to a state-of-the-art GPGPU-based solution. Compared to a state-of-the-art graph analytics accelerator, GraphDynS also achieves 1.9× speedup and 1.8× less energy on average using the same memory … dartmouth course list