Search Results for author:
Found 4 papers, 1 papers with code
Proteus: Preserving Model Confidentiality during Graph Optimizations
Development of new DL models typically involves two parties: the model developers and performance optimizers.
Accelerating Graph Neural Networks on Real Processing-In-Memory Systems
Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kernels, the latter dominates the total time, being significantly bottlenecked by data movement between memory and processors.
Minuet: Accelerating 3D Sparse Convolutions on GPUs
Minuet proposes to (i) replace the hash tables used in the Map step with a novel segmented sorting double-traversed binary search algorithm that highly utilizes the on-chip memory hierarchy of GPUs, (ii) use a lightweight scheme to autotune the tile size in the Gather and Scatter operations of the GMaS step, such that to adapt the execution to the particular characteristics of each SC layer, dataset, and GPU architecture, and (iii) employ a padding-efficient GEMM grouping approach that reduces both memory padding and kernel launching overheads.
The Synergy of Speculative Decoding and Batching in Serving Large Language Models
Large Language Models (LLMs) like GPT are state-of-the-art text generation models that provide significant assistance in daily routines.
