A joint code-and-data decomposition pipeline maps the LULESH proxy application onto the Cerebras WSE, measured up to 4.8x faster than an NVIDIA A100, with analytical models predicting runtime within ~50%.
The spatial computer: A model for energy-efficient parallel computation
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abstract
We present a new parallel model of computation suitable for spatial architectures, for which the energy used for communication heavily depends on the distance of the communicating processors. In our model, processors have locations on a conceptual two-dimensional grid, and their distance therein determines their communication cost. In particular, we introduce the energy cost of a spatial computation, which measures the total distance traveled by all messages, and study the depth of communication, which measures the largest number of hops of a chain of messages. We show matching energy lower- and upper bounds for many foundational problems, including sorting, median selection, and matrix multiplication. Our model does not depend on any parameters other than the input shape and size, simplifying algorithm analysis. We also show how to simulate PRAM algorithms in our model and how to obtain results for a more complex model that introduces the size of the local memories of the processors as a parameter.
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cs.DC 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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Unstructured Hydrodynamics on Spatial Dataflow Architectures: A Joint Code and Data Decomposition Approach
A joint code-and-data decomposition pipeline maps the LULESH proxy application onto the Cerebras WSE, measured up to 4.8x faster than an NVIDIA A100, with analytical models predicting runtime within ~50%.