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Trends indicate that emerging SmartNICs, either from different vendors or generations from the same vendor, exhibit substantial differences in hardware parallelism and memory interconnects. These variations make porting programs across NICs highly complex and time-consuming, requiring programmers to significantly refactor code for performance based on each target NIC’s hardware characteristics. We argue that an ideal SmartNIC compilation framework should allow developers to write target-independent programs, with the compiler automatically managing cross-NIC porting and performance optimization. We present such a framework, Alkali, that achieves this by (1) proposing a new intermediate representation for building flexible compiler infrastructure for multiple NIC targets and (2) developing a new iterative parallelism optimization algorithm that automatically ports and parallelizes the input programs based on the target NIC’s hardware characteristics. Experiments across a wide range of NIC applications demonstrate that Alkali enables developers to easily write portable, high-performance NIC programs. Our compiler optimization passes can automatically port these programs and make them run efficiently across all targets, achieving performance within 9.8% of hand-tuned expert implementations. 

Upcoming imaging surveys will allow for high signal-to-noise measurements of galaxy clustering at small scales. In this work, we present the results of the Rubin Observatory Legacy Survey of Space and Time (LSST) bias challenge, the goal of which is to compare the performance of different nonlinear galaxy bias models in the context of LSST Year 10 (Y10) data. Specifically, we compare two perturbative approaches, Lagrangian perturbation theory (LPT) and Eulerian perturbation theory (EPT) to two variants of Hybrid Effective Field Theory (HEFT), with our fiducial implementation of these models including terms up to second order in the bias expansion as well as nonlocal bias and deviations from Poissonian stochasticity. We consider a variety of different simulated galaxy samples and test the performance of the bias models in a tomographic joint analysis of LSST-Y10-like galaxy clustering, galaxy-galaxy-lensing and cosmic shear. We find both HEFT methods as well as LPT and EPT combined with non-perturbative predictions for the matter power spectrum to yield unbiased constraints on cosmological parameters up to at least a maximal scale ofk_max = 0.4 Mpc^-1for all samples considered, even in the presence of assembly bias. While we find that we can reduce the complexity of the bias model for HEFT without compromising fit accuracy, this is not generally the case for the perturbative models. We find significant detections of non-Poissonian stochasticity in all cases considered, and our analysis shows evidence that small-scale galaxy clustering predominantly improves constraints on galaxy bias rather than cosmological parameters. These results therefore suggest that the systematic uncertainties associated with current nonlinear bias models are likely to be subdominant compared to other sources of error for tomographic analyses of upcoming photometric surveys, which bodes well for future galaxy clustering analyses using these high signal-to-noise data.