Cerebras Graph Compiler

The Cerebras Graph Compiler (CGC) takes as input a user-specified neural network. For maximum workflow familiarity and flexibility, researchers can use both existing ML frameworks — such as TensorFlow and PyTorch and well-structured graph algorithms written in other general-purpose languages, such as C and Python, to program the CS-1. To translate a deep learning network into an optimized executable, CGC extracts a static graph representation of the problem from the source language and converts it into the Cerebras Linear Algebra Intermediate Representation (CLAIR).

As ML frameworks evolve rapidly to keep up with the needs of the field, this consistent input abstraction allows CGC to quickly support new frameworks and features, without changes to the underlying compiler. Once the CLAIR graph has been extracted, CGC performs a matching and covering operation that matches subgraphs to kernels from the Cerebras kernel library. These kernels are optimized to provide high-performance compute at extremely low latency on the fabric of the WSE. The result of this matching operation is a kernel graph. CGC then allocates compute and memory to each kernel in the graph and maps every kernel onto a physical region of the computational array of cores. Finally, a communication path, unique to each network, is configured onto the fabric. During this compilation process, kernel placement is formulated as a multi-constraint problem on 1) memory capacity and bandwidth, 2) computation requirements, and 3) communication costs. The placement engine then takes into account both algorithmic efficiency and compute core utilization to generate a result that maximizes locality, minimizes routing distances, and avoids hotspots. The final result is a CS-1 executable, customized to the unique needs of each neural network, so that all 400,000 SLAC cores and 18 Gigabytes of on-chip SRAM can be used at maximum utilization towards accelerating the deep learning application. 

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