Papers

• Pie: A Programmable Serving System for Emerging LLM Applications (2025)
  In Gim et al.
  3.10
• Pearl: Automatic Code Optimization Using Deep Reinforcement Learning (2025)
  Djamel Rassem Lamouri et al.
  2.87
• CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe (2026)
  Tara Saba et al.
  1.83
• SemanticForge: Repository-Level Code Generation through Semantic Knowledge Graphs and Constraint Satisfaction (2025)
  Wuyang Zhang et al.
  1.56
• Synergy-Guided Compiler Auto-Tuning of Nested LLVM Pass Pipelines (2025)
  Haolin Pan et al.
  1.50
• Comparative analysis of large data processing in Apache Spark using Java, Python and Scala (2025)
  Ivan Borodii et al.
  1.50
• Dato: A Task-Based Programming Model for Dataflow Accelerators (2025)
  Shihan Fang et al.
  1.44
• GroupTuner: Efficient Group-Aware Compiler Auto-Tuning (2025)
  Bingyu Gao et al.
  1.22
• BurTorch: Revisiting Training from First Principles by Coupling Autodiff, Math Optimization, and Systems (2025)
  Konstantin Burlachenko et al.
  1.11
• Tempo: Compiled Dynamic Deep Learning with Symbolic Dependence Graphs (2025)
  Pedro F. Silvestre et al.
  1.00
• Alto: Orchestrating Distributed Compound AI Systems with Nested Ancestry (2024)
  Deepti Raghavan et al.
  0.50
• ACPO: AI-Enabled Compiler Framework (2023)
  Amir H. Ashouri et al.
  0.33
• Field-Programmable Deep Neural Network (DNN) Learning and Inference accelerator: a concept (2018)
  Luiz M Franca-Neto
  —
• Compiler-assisted Adaptive Program Scheduling in big.LITTLE Systems (2019)
  Marcelo Novaes et al.
  —
• HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search (2021)
  Xiangzhong Luo et al.
  —
• End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning (2022)
  Yao Xiao et al.
  —
• Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep Learning (2024)
  Wei An et al.
  —