Ultrafast CPU/GPU Kernels for Density Accumulation in Placement


Density accumulation is a widely-used primitive operation in physical design, especially for placement. Iterative invocation in the optimization flow makes it one of the runtime bottlenecks. Accelerating density accumulation is challenging due to data dependency and workload imbalance. In this paper, we propose efficient CPU/GPU kernels for density accumulation by decomposing the problem into two phases: constant-time density collection for each instance and a linear-time prefix sum. We develop CPU and GPU dedicated implementations, and demonstrate promising efficiency benefits on tasks from large-scale placement problems.

58th ACM/IEEE Design Automation Conference (DAC) 2021
Zizheng Guo
Zizheng Guo
Ph.D. Student

I am a Ph.D. candidate at Peking University. My research interests include data structures, algorithm design and GPU acceleration for combinatorial optimization problems.