37 citations · 105 across the 12 of their papers we have counts for
12 papers
Programming Bare-Metal Accelerators with Heterogeneous Threading Models: A Case Study of Matrix-3000
Jianbin Fang, Peng Zhang, Chun Huang +4
As the hardware industry moves towards using specialized heterogeneous many-cores to avoid the effects of the power wall, software developers are finding it hard to deal with the c…
Parallel Programming Models for Heterogeneous Many-Cores : A Survey
Jianbin Fang, Chun Huang, Tao Tang +1
Heterogeneous many-cores are now an integral part of modern computing systems ranging from embedding systems to supercomputers. While heterogeneous many-core design offers the pote…
Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures: A Machine Learning Based Approach
Peng Zhang, Jianbin Fang, Canqun Yang +3
This article presents an automatic approach to quickly derive a good solution for hardware resource partition and task granularity for task-based parallel applications on heterogen…
Characterizing Scalability of Sparse Matrix-Vector Multiplications on Phytium FT-2000+ Many-cores
Donglin Chen, Jianbin Fang, Chuanfu Xu +2
Understanding the scalability of parallel programs is crucial for software optimization and hardware architecture design. As HPC hardware is moving towards many-core design, it bec…
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference
Qing Qin, Jie Ren, Jialong Yu +6
The recent advances in deep neural networks (DNNs) make them attractive for embedded systems. However, it can take a long time for DNNs to make an inference on resource-constrained…
Optimizing Sparse Matrix-Vector Multiplication on Emerging Many-Core Architectures
Shizhao Chen, Jianbin Fang, Donglin Chen +2
Sparse matrix vector multiplication (SpMV) is one of the most common operations in scientific and high-performance applications, and is often responsible for the application perfor…
Tuning Streamed Applications on Intel Xeon Phi: A Machine Learning Based Approach
Peng Zhang, Jianbin Fang, Tao Tang +2
Many-core accelerators, as represented by the XeonPhi coprocessors and GPGPUs, allow software to exploit spatial and temporal sharing of computing resources to improve the overall…
Streaming Applications on Heterogeneous Platforms
Zhaokui Li, Jianbin Fang, Tao Tang +2
Using multiple streams can improve the overall system performance by mitigating the data transfer overhead on heterogeneous systems. Currently, very few cases have been streamed to…
Efficient and High-quality Sparse Graph Coloring on the GPU
Xuhao Chen, Pingfan Li, Jianbin Fang +3
Graph coloring has been broadly used to discover concurrency in parallel computing. To speedup graph coloring for large-scale datasets, parallel algorithms have been proposed to le…
Evaluating the Performance Impact of Multiple Streams on the MIC-based Heterogeneous Platform
Zhaokui Li, Jianbin Fang, Tao Tang +3
Using \textit{multiple streams} can improve the overall system performance by mitigating the data transfer overhead on heterogeneous systems. Prior work focuses a lot on GPUs but l…
NEMO5: Achieving High-end Internode Communication for Performance Projection Beyond Moore's Law
Robert Andrawis, Jose David Bermeo, James Charles +12
Electronic performance predictions of modern nanotransistors require nonequilibrium Green's functions including incoherent scattering on phonons as well as inclusion of random allo…
An Empirical Study of Intel Xeon Phi
Jianbin Fang, Ana Lucia Varbanescu, Henk Sips +3
With at least 50 cores, Intel Xeon Phi is a true many-core architecture. Featuring fairly powerful cores, two cache levels, and very fast interconnections, the Xeon Phi can get a t…