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Lenore M Mullin

Publications and source records attributed to Lenore M Mullin.

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Testing the EPYC Conjecture on Real Hardware: MoA-Guided Dense Matrix Multiplication on NCSA Delta (AMD EPYC 7763 Milan)

A companion empirical study conjectured that MoA-guided dense matrix multiplication would need per-CCD recalibration on AMD EPYC Bergamo; allocation access to that machine was declined, and this paper reports the resulting test on NCSA Delta, an architecturally related chip. Its central claim: every result here traces to shape, not architecture in the abstract -- cache shape, access-pattern shape, and co-tenancy itself treated as a shape parameter. The corrected block size M_C = 256 follows directly from Delta's real 512 KB L2 divided by the data type's own byte width, no parameter fit after the fact, and outperforms the M1-Pro-inherited M_C = 64 by 30--59% -- a block's shape finally matching the cache shape it was always meant to occupy. The same reasoning extends to a shared level: co-tenancy treated as raw capacity fails by an order of magnitude, while co-tenancy treated as a shape parameter -- capacity divided by concurrent consumers -- survives. A controlled NUMA experiment isolates a third shape: GEBP and MoA-pipelined compute the identical result at identical achieved bandwidth, yet GEBP loses 13.9% of its throughput to remote memory while MoA-pipelined loses only 2.4% -- not a speed difference, a difference in the shape each kernel's access pattern traces through memory. The headline result reproduces the M1 Pro's exact three-of-four win over a Strassen-GEBP hybrid, a pattern AWS Graviton4 did not achieve. One question is named rather than resolved: whether Delta's larger MoA-over-GEBP margin reflects genuine speed or an unmatched utilization fraction. Every result here was predicted before it was measured, not fitted after.

cs.PF

Machine Shape and Hierarchical Blocking: A Mathematics of Arrays Formalization, with an Open Problem in Hierarchical Shape Occupancy

A companion empirical study found that dense matrix multiplication block sizes calibrated on Apple M1 Pro correspond to two cache-t formulas that mispredict badly on a dierent chip's known cache sizes. This paper formalizes the question that nding raises. We extend the Mathematics of Arrays (MoA) framework's array-shape derivation operator to a new operator that derives a hierarchical, multi-level blocking and prefetch schedule from a machine's shape: an ordered sequence of cache-level capacities, bandwidths, and occupancy fractions. This operator recovers the calibrated values on every one of three real machines tested to date as a special case, reducing each machine's unknowns to a small number of level-specic occupancy fractions. We then state precisely, without claiming to resolve, the paper's central open problem: whether those fractions are derivable from more primitive properties co-tenancy, private-cache-level count, associativity, prefetcher behavior or are fundamentally per-architecture constants. Four falsiable hypotheses are stated and tested against real hardware, with mixed results. We further state two limits of the framework explicitly: it requires dedicated, non-virtualized hardware access to be well-dened at all, and it extends only partway to a distributed-memory network, where realizing a tile across nodes requires a separate choice of communication algorithm the framework does not itself make. A rst, honest attempt at extending the framework toward predicting throughput directly, not just block size, closes the paper: two terms prove derivable from a specication sheet, one requires a single measurement, and one tested across three machines does not yet transfer between them.

cs.PF