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Graphics Processing Units (GPUs) are increasingly used to accelerate scientific applications. The state-of-the-art limits the adaptability of GPU kernels to both problem parameters and hardware characteristics. This makes writing high performance libraries for GPUs challenging. We address these challenges through Kernel Specialization (KS) which supports both user and hardware parameters and produces...
Graphics processing units (GPUs) offer significant speedups over CPUs for certain classes of applications. However, programming for GPUs is challenging. There are many parameters that affect performance and their values may change depending on both problem instance and GPU hardware specifics. In addition, most GPU kernels are compiled once, performance optimizations are applied at application compile...
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