Sparse linear algebra operators are memory bound due to low compute to memory access ratio and irregular data access patterns. The exceptional bandwidth improvement provided by the emerging high-bandwidth memory (HBM) technologies, coupled with the ability of FPGAs to customize the memory hierarchy and compute engines, brings the potential to significantly boost the performance of sparse linear algebra operators.In this paper we identify four challenges when developing highperformance sparse linear algebra accelerators on HBM-equipped FPGAs -low HBM bandwidth utilization with conventional sparse storage, limited on-chip memory capacity being the bottleneck when scaling to multiple HBM channels, low compute occupancy due to bank conflicts and inter-iteration carried dependencies, and timing closure on multi-die heterogeneous fabrics. We conduct an in-depth case study on sparse matrix-vector multiplication (SpMV) to explore techniques that tackle the four challenges. These techniques include (1) a customized sparse matrix format tailored for HBMs, (2) a scalable on-chip buffer design that combines replication and banking, (3) best practices of using HLS to implement hardware modules that dynamically resolve bank conflicts and carried dependencies for achieving high compute occupancy, and (4) a splitkernel design methodology for frequency optimization. Using the techniques, we demonstrate HiSparse, a high-performance SpMV accelerator on a multi-die HBM-equipped FPGA device. We evaluated HiSparse on a variety of matrix datasets. The results show that HiSparse achieves a high frequency and delivers promising speedup with increased bandwidth efficiency when compared to prior arts on CPUs, GPUs, and FPGAs. HiSparse is available at https://github.com/cornell-zhang/HiSparse.
CCS CONCEPTS• Hardware → Hardware accelerators; • Computer systems organization → Data flow architectures.
To predict the effects of Dual-source CT imaging technology for preoperative patients with transcatheter aortic valve implantation. This is a parallel, randomly allocated to following two groups: patients with the Dual-source CT imaging technology and with conventional imaging technology,
and the clinical material from two groups patients with Aortic valve disease are collected, and the images quality between two group are assessed and then complications in one month after surgery are recorded by follow-up. Our outcomes show that patients by the Dual-source CT imaging technology
show less complications compared to patients by conventional imaging technology and imaging quality is better than that in conventional imaging group. In addition, image noise, and contrast-to-noise ratio are also examined by Dual-source CT imaging. We can conclude that the Dual-source CT
can effectively reduce the complications, and the Dual-source CT can predict effects of preoperative patients with Aortic valve disease and prevent the development of Aortic valve disease (AVD).
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