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PACT 2026: International Conference on Parallel Architectures and Compilation Techniques

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投稿締切日:
2026-04-17
通知日:
2026-08-05
開催日:
2026-10-19
開催地:
Chicago, Illinois, USA
開催回数:
35
CCF: B   ICORE: B   QUALIS: A2   閲覧: 74643   フォロー: 87   参加: 7

会伴インデックス (CP-I)

88.2 / 100
全 5,683 件中 第 92 位 · 上位 2%

ソフトウェア工学 分野 247 件中 第 12 位 システム・アーキテクチャ 分野 341 件中 第 23 位

学術的評価 (35%)
92
投稿の選択性 (20%)
90
開催回数 (20%)
96
コミュニティの注目度 (10%)
67
公開情報の充実度 (15%)
80

使用した入力: 収録ランク：CCF B, ICORE B, QUALIS A2 · 採択率：20.5%（記録のある 5 回の平均） · 確認できる開催回数：35 · 会伴でフォローしている研究者：87 人 · 過去 24 か月にこのページを開いた研究者：24 人

公開情報で不足しているもの: 過去の開催回 (+3.0)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 100% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-09-21

論文募集

PACT 2026 (International Conference on Parallel Architectures and Compilation Techniques) is a CCF B / ICORE B / QUALIS A2 conference held in Chicago, Illinois, USA on 2026-10-19. The paper submission deadline is 2026-04-17. Acceptance notifications are sent on 2026-08-05.

Scope The International Conference on Parallel Architectures and Compilation Techniques (PACT) is a unique technical conference at the intersection of hardware and software, with a special emphasis on parallelism. PACT brings together researchers from computer architectures, compilers, execution environments, programming languages, and applications to present and discuss their latest research results, tools, and practical experiences. This year, PACT is specifically committed to pioneering AI-centric computing, seeking research that redefines the performance, scalability, and efficiency of large-scale AI workloads across diverse parallel and heterogeneous platforms. PACT 2026 will be held as an in-person event in Chicago, IL. USA. We encourage all authors of accepted papers to participate, and at least one author must attend the conference. PACT seeks submissions in two categories: Research Papers Tools and Practical Experience (TPE) Papers Topics of Interest PACT welcomes submissions on topics including, but not limited to: Parallel architectures, including accelerators for AI and other domains Conventional parallel architectures (e.g., multicore, multithreaded, superscalar, and VLIW architectures) and heterogeneous architectures AI accelerators: design of specialized hardware for LLM inference and training (e.g., TPUs, NPUs, and custom silicon) In-memory & near-data processing: architectures to mitigate the “memory wall” in massive AI model parameters Heterogeneous systems: integration of CPUs, GPUs, and FPGAs for distributed AI workloads Scalable AI Infrastructure: Architecture support for multi-node, multi-GPU clusters and high-speed interconnects for LLM scaling Compilers and tools for parallel architectures Conventional compilers and tools for parallel and heterogeneous architectures Dynamic translation and optimization ML compilers: automated optimization, kernel fusion, and code generation for ML frameworks LLMs for compilation: using AI to automate parallelization, loop transformations, and autotuning Dynamic optimization: runtime systems for adaptive AI model execution and sparse computation Quantization & compression: compiler-assisted techniques for model pruning and low-precision arithmetic Middleware and runtime system support for parallel computing Resource management & scheduling Communication & synchronization Energy-aware middleware Quantum-HPC interfacing Serverless parallel computing (e.g., AWS Lambda) AI & LLM-specific runtime support, including distributed inference & training, KV cache management, and computation-communication overlap I/O issues in parallel computing and their application impact Data loading & preprocessing pipelines Metadata scalability Memory-storage convergence Large-scale data processing for AI models and applications Hardware and software resilience & fault tolerance Checkpointing & restart Silent data corruption detection Self-healing runtimes Applications and experimental studies of parallel processing, especially using AI models Parallel programming languages, algorithms, and applications Computational models for concurrent execution Compiler and hardware support for parallel applications Support for correctness in hardware and software Reconfigurable parallel computing Research Papers Research papers will be evaluated by the PACT Program Committee based on: Relevance: The paper should align with PACT’s topics of interest. Novelty/Originality: The work should present new ideas or offer fresh perspectives. Significance: The research should address an important problem and have the potential to influence future work. Results: The claims should be well-supported by clear and validated results. Comparison to Prior Work: The paper should properly discuss existing literature, highlighting similarities, differences, and improvements. Tools and Practical Experience (TPE) Papers TPE papers focus on practical applications, industry challenges, and experience reports. A TPE paper must clearly explain its functionality, summarize practical experience with realistic case studies, and describe any supporting artifacts. The title of a TPE paper must include the prefix “TPE:”. TPE papers follow the same submission guidelines and are reviewed by the same Program Committee as research papers. TPE papers will be evaluated based on: Originality: They should present PACT-related technologies applied to real-world problems. Usability: The tool or software should have broad applicability and aid PACT-related research. Documentation: The tool/software should be well-documented on a public website. Benchmark Repository: A benchmark suite should be provided for testing. Availability: Preference is given to tools/software that are freely available, though industry/commercial tools may be considered with justification. Foundations: The paper should relate to PACT’s principles, though extensive theoretical discussion is not required.
最終更新：Dou Sun（）

採択率

平均採択率: 22.9% 7 年間 (1999–2005).

年投稿数採択数採択率(%)
20051193025.2%
20041222318.9%
20031442416.7%
20021192521%
20011262620.6%
20001072927.1%
19991143530.7%

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