:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Lenadora, Damitha, Sathia, Vimarsh, Gerogiannis, Gerasimos, Yesil, Serif, Torrellas, Josep, Mendis, Charith
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Performance
Online Access:	https://arxiv.org/abs/2306.15155
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning
by: Sudusinghe, Chamika, et al.
Published: (2025)

FLuRKA: Fast and accurate unified Low-Rank & Kernel Attention
by: Gupta, Ahan, et al.
Published: (2023)

Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants
by: You, Bozhi, et al.
Published: (2025)

oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation
by: Li, Jianhui, et al.
Published: (2023)

Efficient Graph Knowledge Distillation from GNNs to Kolmogorov--Arnold Networks via Self-Attention Dynamic Sampling
by: Cui, Can, et al.
Published: (2025)

DECA: A Near-Core LLM Decompression Accelerator Grounded on a 3D Roofline Model
by: Gerogiannis, Gerasimos, et al.
Published: (2025)

COMET: Neural Cost Model Explanation Framework
by: Chaudhary, Isha, et al.
Published: (2023)

Automating Energy-Efficient GPU Kernel Generation: A Fast Search-Based Compilation Approach
by: Zhang, Yijia, et al.
Published: (2024)

The Next 700 ML-Enabled Compiler Optimizations
by: VenkataKeerthy, S., et al.
Published: (2023)

Priority Sampling of Large Language Models for Compilers
by: Grubisic, Dejan, et al.
Published: (2024)

AutoSAGE: Input-Aware CUDA Scheduling for Sparse GNN Aggregation (SpMM/SDDMM) and CSR Attention
by: Stankovic, Aleksandar
Published: (2025)

Accelerating Sparse Ternary GEMM for Quantized ML on Apple Silicon
by: Lipshitz, Baraq, et al.
Published: (2025)

LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization
by: Merouani, Massinissa, et al.
Published: (2025)

PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model Adaptation
by: An, Zihao, et al.
Published: (2025)

GPU-Accelerated INT8 Quantization for KV Cache Compression in Large Language Models
by: Taneja, Maanas, et al.
Published: (2026)

V-Seek: Accelerating LLM Reasoning on Open-hardware Server-class RISC-V Platforms
by: Rodrigo, Javier J. Poveda, et al.
Published: (2025)

Efficient Solving of Large Single Input Superstate Decomposable Markovian Decision Process
by: Mahjoub, Youssef Ait El, et al.
Published: (2025)

ECCO: Evidence-Driven Causal Reasoning for Compiler Optimization
by: Pan, Haolin, et al.
Published: (2026)

SAfEPaTh: A System-Level Approach for Efficient Power and Thermal Estimation of Convolutional Neural Network Accelerator
by: Chen, Yukai, et al.
Published: (2024)

Library Liberation: Competitive Performance Matmul Through Compiler-composed Nanokernels
by: Thangamani, Arun, et al.
Published: (2025)

EXAQ: Exponent Aware Quantization For LLMs Acceleration
by: Shkolnik, Moran, et al.
Published: (2024)

cedar: Optimized and Unified Machine Learning Input Data Pipelines
by: Zhao, Mark, et al.
Published: (2024)

VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations
by: Zheng, Zhongchun, et al.
Published: (2025)

AutoSP: Unlocking Long-Context LLM Training Via Compiler-Based Sequence Parallelism
by: Gupta, Ahan, et al.
Published: (2026)

PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs
by: Saha, Rappy, et al.
Published: (2026)

ALISA: Accelerating Large Language Model Inference via Sparsity-Aware KV Caching
by: Zhao, Youpeng, et al.
Published: (2024)

GreedySnake: Accelerating SSD-Offloaded LLM Training with Efficient Scheduling and Optimizer Step Overlapping
by: Yin, Yishu, et al.
Published: (2025)

GPU Cluster Scheduling for Network-Sensitive Deep Learning
by: Sharma, Aakash, et al.
Published: (2024)

DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures
by: Yang, Peiming, et al.
Published: (2025)

Accelerating AI Performance using Anderson Extrapolation on GPUs
by: Dajani, Saleem Abdul Fattah Ahmed Al, et al.
Published: (2024)

Accelerating Diffusion LLMs via Adaptive Parallel Decoding
by: Israel, Daniel, et al.
Published: (2025)

Compiler-First State Space Duality and Portable $O(1)$ Autoregressive Caching for Inference
by: Santoni, Cosmo
Published: (2026)

Accelerating Mobile Inference through Fine-Grained CPU-GPU Co-Execution
by: Li, Zhuojin, et al.
Published: (2025)

Automatic Generation of Fast and Accurate Performance Models for Deep Neural Network Accelerators
by: Lübeck, Konstantin, et al.
Published: (2024)

SparAMX: Accelerating Compressed LLMs Token Generation on AMX-powered CPUs
by: AbouElhamayed, Ahmed F., et al.
Published: (2025)

It's all about PR -- Smart Benchmarking AI Accelerators using Performance Representatives
by: Jung, Alexander Louis-Ferdinand, et al.
Published: (2024)

FlexiSAGA: A Flexible Systolic Array GEMM Accelerator for Sparse and Dense Processing
by: Müller, Mika Markus, et al.
Published: (2025)

LiveTune: Dynamic Parameter Tuning for Feedback-Driven Optimization
by: Shabgahi, Soheil Zibakhsh, et al.
Published: (2023)

BanditQ: Fair Bandits with Guaranteed Rewards
by: Sinha, Abhishek
Published: (2023)

Towards Computational Performance Engineering for Unsupervised Concept Drift Detection -- Complexities, Benchmarking, Performance Analysis
by: Werner, Elias, et al.
Published: (2023)