Leveraging intrinsic data priors is critical for effective data recovery. However, existing approaches often struggle to achieve theoretical guarantees, strong performance, and computational efficiency simultaneously. In this paper, we introduce a novel Representative Coefficient Correlated Total Variation (RCCTV) regularizer that captures the recently observed low-rank and local smoothness properties of the representative coefficient tensor derived from a low-rank decomposition. RCCTV regularizer offers three key advantages: (1) it operates on a compact representative coefficient image significantly smaller than the original data, enabling highly efficient optimization; (2) it jointly enforces low-rankness and spatial smoothness through a single regularizer, eliminating the need for trade-off parameters; and (3) when integrated into a robust PCA framework (i.e., RCCTV-RPCA model), it admits provable exact recovery under mild conditions. To solve the resulting model, we develop an efficient ADMM-based algorithm accelerated via fast Fourier transform. Extensive experiments on both synthetic and real-world datasets demonstrate that the RCCTV-RPCA model achieves state-of-the-art accuracy while running significantly faster. Our code and Supplementary Material are available at https://github.com/mendy-2013/RCCTV.

CUDA’s programming model, exposing massive parallelism via fine-grained scalar threads, has become the de facto standard for GPU computing. Concurrently, NPUs are emerging as highly efficient accelerators, but their architecture is fundamentally different, relying on coarse-grained, explicit 2-D tile-based instructions. This creates a critical challenge: bridging the semantic gap "From Threads to Tiles". A direct translation is infeasible, as it requires lifting the implicit parallelism of CUDA’s scalar model into the explicit, multi-dimensional vector space of NPUs, a problem we formalize as a lifting challenge.This paper introduces T2T, a compiler framework that automates this "Threads to Tiles" translation via the 2-D Vectorization technique. T2T first transforms a CUDA kernel’s implicit SIMT parallelism into a structured, explicit loop nest via our Unified Parallelism Abstraction (UPA), making the parallelism analyzable. From this representation, T2T’s core vectorization engine systematically selects optimal pairs of loops and maps them onto the NPU’s 2-D tile instructions to maximize hardware utilization. To ensure correctness and handle performance-critical CUDA features, a final set of semantics-preserving optimizations is applied, including efficient control-flow management and vectorization of warp-level intrinsics.We implement T2T based on Polygeist and evaluate representative NPU architectures. On a diverse set of benchmarks, kernels translated by T2T achieve up to 73% of native CUDA performance on an A100 GPU and outperform baseline translation approaches by up to 6.9×. Our work demonstrates that a systematic, compiler-driven approach to 2-D vectorization is a principled and high-performance path for porting the rich CUDA ecosystem to the evolving landscape of NPU accelerators.

Recent multimodal large language models (MLLMs) marry modality-specific vision or audio encoders with a shared text decoder. While the encoder is compute- intensive but memory-light, the decoder is the opposite, yet state-of-the-art serving stacks still time-multiplex these complementary kernels, idling SMs or HBM in turn. We introduce SpaceServe, a serving system that space-multiplexes MLLMs: it decouples all modality encoders from the decoder, and co-locates them on the same GPU using fine-grained SM partitioning available in modern runtimes. A cost-model-guided Space-Inference Scheduler (SIS) dynamically assigns SM slices, while a Time-Windowed Shortest-Remaining-First (TWSRFT) policy batches en- coder requests to minimise completion latency and smooth decoder arrivals. Evaluation shows that SpaceServe reduces time-per-output-token by 4.81× on average and up to 28.9× on Nvidia A100 GPUs. SpaceServe is available at https://github.com/gofreelee/SpaceServe