Scientific Computing
Scientific computing is a field of study that uses advanced computational methods and algorithms to solve complex scientific problems. It draws heavily from mathematics, computer science, physics, engineering and other fields in order to develop numerical models and simulations that are used to understand the behavior of real-world processes. Scientific computing can also be used to collect, analyze, visualize and interpret data generated by experiments or observational studies. The ultimate goal is to gain insight into the functioning of natural phenomena and help inform decisions on how to best manage our planet’s resources. Through its advances in technology, scientific computing has become an essential tool for furthering knowledge and making decisions in a variety of fields such as climate science, astronomy, biology and medicine. It is an ever-evolving field as more powerful computers and more efficient algorithms are developed to tackle increasingly complex problems.
Scientific computing powers research and discovery in fundamental physics and life sciences, leading to important advances enrich our daily lives. But the workloads that drive these applications require massive sparse computation with high bandwidth memory access and communication. Even supercomputers are challenged by this work.
Cerebras Systems’ CS-1 and CS-2 delivers supercomputer-scale acceleration in a single system for traditional scientific computing workloads, AI surrogate workloads, or HPC+AI cognitive simulations that leverage both. Cerebras has showcased impressive results within the areas of computational fluid dynamics, AI-accelerated modeling & simulation, molecular dynamics, and others. The CS-1 allowed Argonne National Laboratory to reduce the experiment turnaround time on cancer prediction models by 300x, ultimately enabling the exploration of scientific questions that would have taken years, in mere months.
