As demand for accelerator hardware increases, companies offering AI and machine learning services must ensure that their owned or rented GPUs are utilized to their fullest potential. We are experienced in identifying and eliminating performance bottlenecks in the compute stack and in tailoring generic solutions to specific hardware and requirements.
In the past, we have successfully collaborated with larger organizations to achieve their performance targets. Using benchmarks and the profiling and diagnostic tools available on the platform, we identify the parts of the system that have the greatest impact on end-to-end runtime, whether the bottleneck is a single GPU kernel, a series of kernels, memory access patterns, or inter-node communication. Often, small changes can bring major improvements. We can navigate the open-source compute ecosystem and tailor libraries to specific use cases or architectures.
