Jae Hyung Ju

Hritvik Taneja, Anish Saxena, Abhishek Revinipati et al.

The rise of reasoning models and agentic systems has made LLM token-generation latency a key bottleneck. Unlike chatbots, whose latency gains saturate at human reading speed, these systems generate intermediate reasoning tokens not consumed by humans. Thus, per-token latency directly determines end-to-end response time. Low-latency inference uses minimal batching, making token generation bandwidth-bound. Tensor Parallelism addresses this by sharding model weights across GPUs and loading them in parallel. However, scaling to more GPUs introduces All-Reduce overheads that grow with GPU count. Removing All-Reduce improves token throughput by 43% for Llama-3.1-8B on 8 H200 GPUs. We propose Synchronization-Free All-Reduce (SiFAR), which reduces synchronization overhead during low-latency inference. Existing oneshot and twoshot algorithms incur overheads from barriers before and after communication. First, we find that the bottom barrier in oneshot enforces a WAW dependency and eliminate it by co-designing communication and model execution to enable dual buffering. However, oneshot scales poorly with GPU count. Twoshot performs better at higher TP degrees but incurs an unavoidable bottom barrier. To overcome this, we leverage in-switch reduction in modern switches. We propose redundant pull, where each GPU reduces the full All-Reduce payload at the switch. This improves oneshot scalability while retaining its no-bottom-barrier advantage. Finally, to reduce top-barrier overhead, we observe that each decode step issues multiple All-Reduce operations, keeping GPUs tightly synchronized after the first. We therefore propose speculative reduction, which initiates data transfer before the top barrier and ensures correctness via lightweight validation. SiFAR reduces All-Reduce latency by up to 52% and improves end-to-end throughput by 18.6% for Llama-3.1-8B and 13.1% for Qwen3.5-397B-17B at TP=8.

Euijun Chung, Jae Hyung Ju, Hyesoon Kim

Multi-chiplet GPUs scale compute throughput and high-bandwidth memory (HBM) capacity, but their non-uniform memory system makes locality between chiplets and their data critical to the GPU's performance and energy efficiency. Locality-aware scheduling and data placement identify which data should reside near each chiplet. However, in general matrix multiplication (GEMM), locality-aware data placement often becomes incompatible with a fixed page-granularity data interleaving, since the optimal granularity for mapping data across chiplets varies widely across workloads. We propose Chiplet-Contiguous Layout, a global memory layout that stores chiplet-local data contiguously. Chiplet-Contiguous Layout enables locality-aware placement compatible with page-granularity placement across diverse large language model (LLM) GEMM shapes, without changes to the operating system or hardware. On representative LLM inference and training GEMMs from Qwen 3 30B and Llama 3.1 70B, Chiplet-Contiguous Layout on average reduces remote HBM traffic by 24.7x on Qwen and 19.2x on Llama over 4KB interleaving, and by 4.1x and 2.1x over coarse locality-aware placement.

Jae Hyung Ju, Jaiyoung Park, Jongmin Kim et al.

Fully homomorphic encryption (FHE) is a promising cryptographic primitive for realizing private neural network inference (PI) services by allowing a client to fully offload the inference task to a cloud server while keeping the client data oblivious to the server. This work proposes NeuJeans, an FHE-based solution for the PI of deep convolutional neural networks (CNNs). NeuJeans tackles the critical problem of the enormous computational cost for the FHE evaluation of CNNs. We introduce a novel encoding method called Coefficients-in-Slot (CinS) encoding, which enables multiple convolutions in one HE multiplication without costly slot permutations. We further observe that CinS encoding is obtained by conducting the first several steps of the Discrete Fourier Transform (DFT) on a ciphertext in conventional Slot encoding. This property enables us to save the conversion between CinS and Slot encodings as bootstrapping a ciphertext starts with DFT. Exploiting this, we devise optimized execution flows for various two-dimensional convolution (conv2d) operations and apply them to end-to-end CNN implementations. NeuJeans accelerates the performance of conv2d-activation sequences by up to 5.68 times compared to state-of-the-art FHE-based PI work and performs the PI of a CNN at the scale of ImageNet within a mere few seconds.