Suchergebnisse - Mathematics of computing Mathematical software Mathematical software performance

An Input-Aware Sparse Tensor Compiler Empowered by Vectorized Acceleration von He, Xianhao, Wang, Haotian, Zhang, Jiapeng, Yang, Wangdong, Chronopoulos, Anthony Theodore, Li, Kenli

“… Sparse matrix-matrix multiplication (SpMM) is a representative operator in sparse computations, whose performance is often limited by the design of sparse formats and the extent of hardware architecture optimization …”
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Tackling the Matrix Multiplication Micro-Kernel Generation with Exo von Castello, Adrian, Bellavita, Julian, Dinh, Grace, Ikarashi, Yuka, Martinez, Hector

“… The GEMM is usually implemented following the GotoBLAS philosophy, which tiles the GEMM operands and uses a series of nested loops for performance improvement …”
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SSpMV: A Sparsity-aware SpMV Framework Empowered by Multimodal Machine Learning von Lin, Shengle, Liu, Chubo, Ding, Yan, Zhou, Joey Tianyi, Li, Kenli, Yang, Wangdong

“… Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse operation in scientific computing and artificial intelligence …”
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pSyncPIM: Partially Synchronous Execution of Sparse Matrix Operations for All-Bank PIM Architectures von Baek, Daehyeon, Hwang, Soojin, Huh, Jaehyuk

“… Recent commercial incarnations of processing-in-memory (PIM) maintain the standard DRAM interface and employ the all-bank mode execution to maximize bank-level …”
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ReSMiPS: A ReRAM-based Sparse Mixed-precision Solver with Fast Matrix Reordering Algorithm von Fu, Yuyang, Li, Jiancong, Chen, Jia, Zhou, Zhiwei, Zhou, Houji, Peng, Wenlong, Li, Yi, Miao, Xiangshui

“… The solution of sparse matrix equations is essential in scientific computing. However, traditional solvers on digital computing platforms are limited by memory bottlenecks in largescale sparse matrix storage and computation …”
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JITSPMM: Just-in-Time Instruction Generation for Accelerated Sparse Matrix-Matrix Multiplication von Fu, Qiang, Rolinger, Thomas B., Huang, H. Howie

“… Achieving high performance for Sparse Matrix-Matrix Multiplication (SpMM) has received increasing research attention, especially on multi-core CPUs, due to the large input data size in applications such as graph neural networks (GNNs …”
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Pipirima: Predicting Patterns in Sparsity to Accelerate Matrix Algebra von Bakhtiar, Ubaid, Joo, Donghyeon, Asgari, Bahar

“… ; a performance bottleneck. To solve such challenges, our key insight is that if while reading/streaming compressed sparse matrices we can quickly anticipate the locations of the non-zero values in a sparse matrix …”
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SpV8: Pursuing Optimal Vectorization and Regular Computation Pattern in SpMV von Li, Chenyang, Xia, Tian, Zhao, Wenzhe, Zheng, Nanning, Ren, Pengju

“… Sparse Matrix-Vector Multiplication (SpMV) plays an important role in many scientific and industry applications, and remains a well-known challenge due to the …”
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InnerSP: A Memory Efficient Sparse Matrix Multiplication Accelerator with Locality-Aware Inner Product Processing von Baek, Daehyeon, Hwang, Soojin, Heo, Taekyung, Kim, Daehoon, Huh, Jaehyuk

“… Sparse matrix multiplication is one of the key computational kernels in large-scale data analytics. However, a naive implementation suffers from the overheads …”
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FeKAN: Efficient Kolmogorov-Arnold Networks Accelerator Using FeFET-based CAM and LUT von Yu, Xuliang, Qian, Yu, Yin, Xunzhao, Zhuo, Cheng, Zhao, Liang

“… First, we develop a software-hardware co-optimized framework for mapping B-spline basis functions (BBF …”
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Accelerating Fourier and Number Theoretic Transforms using Tensor Cores and Warp Shuffles von Durrani, Sultan, Chughtai, Muhammad Saad, Hidayetoglu, Mert, Tahir, Rashid, Dakkak, Abdul, Rauchwerger, Lawrence, Zaffar, Fareed, Hwu, Wen-mei

“… ), are two important mathematical tools having applications in several areas of science and engineering …”
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A Tensor Algebra Compiler for Sparse Differentiation von Shaikhha, Amir, Huot, Mathieu, Hashemian, Shideh

“… with AD-agnostic domain-specific optimizations followed by efficient C++ code generation. We showcase the effectiveness of our framework in terms of performance …”
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Finding the Pareto Frontier of Low-Precision Data Formats and MAC Architecture for LLM Inference von Crafton, Brian, Peng, Xiaochen, Sun, Xiaoyu, Lele, Ashwin, Zhang, Bo, Khwa, Win-San, Akarvardar, Kerem

“… To accelerate AI applications, numerous data formats and physical implementations of matrix multiplication have been proposed, creating a complex design space …”
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Seer: Predictive Runtime Kernel Selection for Irregular Problems von Swann, Ryan, Osama, Muhammad, Sangaiah, Karthik, Mahmud, Jalal

“… Modern GPUs are designed for regular problems and suffer from load imbalance when processing irregular data. Prior to our work, a domain expert selects the …”
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Optimized Polynomial Multiplier Architectures for Post-Quantum KEM Saber von Basso, Andrea, Roy, Sujoy Sinha

“… A significant portion of Saber's computation time is spent on computing polynomial multiplications in polynomial rings with powers-of-two moduli …”
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SFLU: Synchronization-Free Sparse LU Factorization for Fast Circuit Simulation on GPUs von Zhao, Jianqi, Wen, Yao, Luo, Yuchen, Jin, Zhou, Liu, Weifeng, Zhou, Zhenya

“… Sparse LU factorization is one of the key building blocks of sparse direct solvers and often dominates the computing time of circuit simulation programs …”
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Generating Portable High-Performance Code via Multi-Dimensional Homomorphisms von Rasch, Ari, Schulze, Richard, Gorlatch, Sergei

“… We address a key challenge in programming high-performance applications - achieving portable performance, i.e …”
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SpMMPlu: A Compiler Plug-in with Sparse IR for Efficient Sparse Matrix Multiplication von Yang, Tao, Zhou, Yiyuan, Tang, Qidong, Xu, Feng, Ma, Hui, Zhao, Jieru, Jiang, Li

“… Sparsity is becoming arguably the most critical dimension to explore for efficiency and scalability as deep learning models grow significantly larger …”
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A data locality-aware design framework for reconfigurable sparse matrix-vector multiplication kernel von Sicheng Li, Yandan Wang, Wujie Wen, Yu Wang, Yiran Chen, Hai Li

“… For performance improvement, software libraries designated for SpMV computation have been introduced, e.g …”
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Sparso: Context-driven optimizations of sparse linear algebra von Hongbo Rong, Jongsoo Park, Lingxiang Xiang, Anderson, Todd A., Smelyanskiy, Mikhail

“… The sparse matrix is a key data structure in various domains such as high-performance computing, machine learning, and graph analytics …”
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