Seismic changes are underway in the photomask and semiconductor industry, driving the need for GPU acceleration to enable simulation-based processing in reasonable run times. D2S GPU-accelerated solutions are deployed in production settings by leading semiconductor equipment manufacturers worldwide.
Scientific computing, and for that matter all of high performance computing, is quickly migrating to GPU acceleration. Because clock speed is no longer scaling, but computing bandwidth continues to scale, computing algorithms written for single-instruction, multiple data (SIMD) architectures has been and will continue to scale along with Moore’s Law. Both GPU-accelerated algorithms and CPU-only algorithms utilize coarse-level parallelization by dividing the chip or mask data into partitions, and computing the partitions in CPU cores or CPU cores accelerated with GPU(s). But each computing unit computes much more data much faster with GPU acceleration. The performance difference increases every node along Moore’s Law.
This is why for simulation of natural effects, for image processing, and for deep learning, GPU acceleration is the superior platform.