Aditya Chatterjee

Aditya Chatterjee

Adjacent GEMM problems that differ by a single 128-element step in N can show 30% different throughput on the same GPU. This pervasive performance ruggedness - invisible to roofline analysis and peak-FLOPs intuition, yet dominant for every non-peak workload - is the subject of this paper. We propose performance ruggedness analysis as an analytical framework complementary to roofline: rather than summarizing GPU performance with a scalar bound, treat the full multidimensional performance surface as the object of study, decompose its texture into mechanism-attributable components and separate software-removable contributions from hardware-bound ones. The framing is directly analogous to deep-learning loss landscapes - a continuous quantity (the idealized time 2MNK / compute_throughput_peak) made rugged by interaction with discrete hardware substrates (tiles, sub-groups, cache lines, DRAM channels). We apply the framework to BF16 NN (no transpose) GEMM on Intel Battlemage (Arc B580, sycl-tla) via a 32,768-configuration sweep (M, N, K) belongs to {128, ..., 4096}^3. The peak is 110.8 TFLOPs at the non-square shape M=3840, N=2048, K=4096 with the default tile size; the initial landscape roughness is 16.8 TFLOPs per 128-step against an ideal of 2.0. A two-stage software stack - (i) best-of-six dynamic tile selection and (ii) a novel dynamic-programming based padding-and-splitting optimizer with O(1) runtime lookup - reduces roughness by 70% and raises mean throughput by 30%. Cross-tile experiments establish that the residual sawtooth period scales exactly with software tile size, ruling out cache set conflicts and attributing the remaining variance to four hardware-bound sources (per-kernel base overhead, wave quantization, DPAS atom geometry and GDDR6 channel-hash interactions).

Xinchen Jin, Aditya Chatterjee, Pranav Kumar et al.

Vision-Language-Action (VLA) policies translate language and visual inputs into robot actions, where their hidden representations directly shape closed-loop behavior. However, mechanistic interpretability tools from language and vision-language models do not transfer cleanly to VLAs: outputs are robot actions rather than human-readable tokens, and interventions can only be tested via expensive closed-loop rollouts. We propose an event-grounded interpretability pipeline that anchors SAE feature analysis to behavioral events rather than text contexts. End-effector keyframes are clustered within each task using visual, state, and temporal cues, linking SAE features to behaviorally salient events and, via optional VLM annotations, to semantic context. To our knowledge, our pipeline is among the first to ground SAE-based VLA analysis in closed-loop behavioral events. Across two simulation architectures and a real-robot study, event-grounded ranking yields the strongest causal effects on OpenVLA and transfers to the continuous action chunks of $π_{0.5}$. SAE is a sparse but imperfect intervention basis: usability varies with architecture and intervention site, and aggressive intervention reveals safety and interpretability limits. Overall, event-grounded SAE analysis emerges as a practical starting point for behavior-anchored VLA interpretability, motivating future work on SAE features beyond action-aligned coordinates, finer-grained closed-loop evaluation, and safe interventions for high-stakes VLA deployments. Code is available at \url{https://github.com/xc-j/Event-SAE}.