arXiv subjects

Yongchan Jung

Publications and source records attributed to Yongchan Jung.

4 recordsLinked to original sources

arXiv2026

Lesion-DDPM: Lesion-Enhanced 3D Diffusion for MS MRI Synthesis

3D FLAIR MRI is widely recommended as one of the standard MRI sequences for brain imaging in multiple sclerosis (MS), but publicly available MS datasets remain relatively small and vary across scanners, acquisition protocols, and lesion patterns. This scarcity and variability hinder the development of robust neuroimaging machine learning models and are particularly challenging for generative models that aim to synthesize images while preserving small, sparse lesions. We propose Lesion-DDPM, a 3D conditional diffusion framework for lesion-aware FLAIR synthesis that incorporates multi-level anatomical mask injection together with a lesion-weighted reconstruction loss to emphasize lesion voxels while maintaining global brain structure. Using a curated subset of the MSLesSeg dataset, we compare Lesion-DDPM with representative state-of-the-art GAN- and diffusion-based models, assessing both image-generation metrics and downstream 3D U-Net segmentation. In our experiments, Lesion-DDPM achieved the lowest lesion-region reconstruction error among all methods. In a downstream 3D U-Net lesion segmentation task, a model trained only on Lesion-DDPM-generated scans and evaluated on real MRIs reached a Dice score of 0.616 compared with 0.569 for the best competing synthetic dataset. When Lesion-DDPM images were added to the real training set, the Dice score further increased to 0.685.

cs.CV↗

arXiv2026

Performance Analysis and Optimization of 3D Generative Diffusion Models across GPU Architectures

Diffusion models have become essential for high-fidelity 3D MRI synthesis, yet their deployment remains constrained by substantial GPU resource demands arising from hundreds of U-Net evaluations per sample and a highly heterogeneous kernel behavior. This paper performs a comprehensive performance analysis of the state-of-the-art medical diffusion model, Med-DDPM, across three generations of NVIDIA architectures to study kernel-level runtime breakdowns, instruction-mix characteristics, memory system utilization, warp-level activities, and profiler priority-score estimates. We show that training is overwhelmingly dominated by cuDNN convolution and implicit-GEMM kernels, with inefficiencies arising from memory-access patterns, tensor-layout conversions, and limited Tensor Core utilization. Guided by these insights, we evaluate two architecture-aware optimizations TF32 Tensor Core activation and a 3D channels-last layout and demonstrate that they reduce SM cycles by up to 100x, cut dynamic instructions by 100x, raise Tensor Core utilization from 1.45 to 9.98x, and increase IPC by 7% on A100, all without degrading synthesis quality.

cs.LG↗

arXiv2026

RH+: Row-Hit-Optimized Scheduling for PIM-based LLM Inference

Large language model inference on processing-in-memory (PIM) architectures promises to break the memory wall by performing multiply-accumulate (MAC) operations directly within HBM3 DRAM banks. Prior work identifies the power constraint timing parameter nCCDAB as the primary performance bottleneck and optimizes scheduling accordingly. We demonstrate that for GEMV operations that dominate autoregressive decoding, the DRAM row cycle time (nRC) is 10 to 11 times larger than nCCDAB. Consequently, nCCDAB is entirely masked, rendering prior nCCDAB-focused optimizations ineffective for these workloads. The root cause is inherited host-centric address interleaving, which forces every all-bank MAC command into a different DRAM row. We propose RH+ scheduling, a simple stride change that keeps 32 consecutive MAC operations within the same row. Cycle-accurate simulation across four LLM workloads shows that RH+ delivers 8-12x speedup, over 74% energy reduction, and up to 52x EDP improvement.

cs.ET↗

arXiv2026

Dead on Arrival: Characterizing and Protecting Against Dead-Entry TLB Misses in GPU Microarchitectures

GPU workloads with large memory footprints frequently suffer from redundant L2 TLB misses in which a recently evicted translation is immediately re-walked at full page-walk cost. We characterize these dead-entry misses across 24 GPU workloads, finding they account for up to 99% of L2 TLB misses in the most TLB-sensitive applications, yet their performance impact varies widely depending on memory access structure. Workloads where warps share the same virtual page suffer from burst amplification, where a single eviction stalls many warps simultaneously waiting for one translation to return. In contrast, workloads where each warp accesses a distinct set of pages face a capacity-overflow problem that no replacement policy can resolve, a distinction validated by huge page experiments. Building on this two-class taxonomy, we design DEPOT (Dead-Entry PrOTection), a 1 KB Bloom filter mechanism that prevents recently evicted translations from being displaced immediately upon reinstallation, delivering up to 72% IPC improvement on interference-driven workloads with zero overhead on others, and composing with the state-of-the-art TLB prefetching and compaction mechanism, for 2 to 7% additional gain.

cs.AR↗