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We present a high performance algorithm for multiplying sparse distributed polynomials using a multicore processor. Each core uses a heap of pointers to multiply parts of the polynomials using its local cache. Intermediate results are written to buffers in shared cache and the cores take turns combining them to form the result. A cooperative approach is used to balance the load and improve scalability, and the extra cache from each core produces a superlinear speedup in practice. We present benchmarks comparing our parallel routine to a sequential version and to the routines of other computer algebra systems.

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