human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "ism2 optimizing irregular shaped matrix matrix multiplication on gpus"):
  Abstract: Linear algebra operations have been widely used in big data analytics and scientific computations. Many works have been done on optimizing linear algebra operations on GPUs with regular-shaped input. However, few works are focusing on fully utilizing GPU resources when the input is not regular-shaped. Current optimizations lack of considering fully utilizing the memory bandwidth and computing power, therefore they could only achieve sub-optimal performance. In this paper, we propose two efficient irregular-shaped matrix-matrix multiplication (GEMM) algorithms on GPUs, called TSM2 and ISM2. Both of them focus on optimizing GEMMs with various input sizes where at least one of the matrices is tall-and-skinny. We implement our proposed algorithms and test on several modern Nvidia GPU micro-architectures. Experiments show that compared to state of the art, our TSM2 speeds up the computation by 1.1x~3x and improves the memory bandwidth utilization and computing power utilization by 8%~47.6% and 7%~37.3%, respectively, when the size of regular matrix is relatively large or medium. Moreover, our ISM2 speeds up the GEMM by 1.1x~3.5x and improve the memory bandwidth utilization by up to 55% when the size of regular matrix is relatively small.

Includes members:
  Member "improving performance of iterative methods by lossy checkponting":
    also connected through other hyperedges:
      (none)
  Member "blasx a high performance level 3 blas library for heterogeneous multi gpu computing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "superneurons dynamic gpu memory management for training deep neural networks")
  Member "kblas an optimized library for dense matrix vector multiplication on gpu accelerators":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical matrix operations on gpus matrix vector multiplication and compression")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.DC(Distributed, Parallel, and Cluster Computing)