10:110:14 (2012). i It allows for robust and repeatable . In: Proceedings of the 2013 IEEE 27th International Symposium on Parallel and Distributed Processing, ser. SuiteSparse Matrix Collection - Texas A&M University Toward a New Metric for Ranking High Performance Computing Systems. Section 4 ends with the incorporation of the kernels in some solvers for systems of linear algebraic equations based on the use of the conjugate gradient method. The benchmarks are divided into three categories: dense matrix linear algebra kernels, sparse matrix linear algebra kernels, and machine learning functionality. The tested matrix dimensions are parameterized by \(N\) with values of \(N\) equal to: 1000, 2000, 4000, 8000, 10000, 15000, and 20000. The D-SAB comprises of two parts: (1) the benchmark algorithms and (2) the sparse matrix set. In the specific context of deep learning, sparsity has emerged as one of the leading approaches for increasing training and inference performance as well as reducing the model sizes while keeping the accuracy. This routine supports CSR, Coordinate (COO), as well as the new Blocked-ELL storage formats. MathSciNet PaCT 2003. 17(1), 12351241 (2016), MathSciNet [4], LIL stores one list per row, with each entry containing the column index and the value. http://dl.acm.org/citation.cfm?id=1251254.1251264, Demmel, J., Eliahu, D., Fox, A., Kamil, S., Lipshitz, B., Schwartz, O., Spillinger, O.: Communication-optimal parallel recursive rectangular matrix multiplication. Microbenchmark parameters that can be specified include the dimensions of the matrices to be performance tested with, number of performance trials per matrix, and the allocator and microbenchmarking functions to be used. SoCC 17. Keywords. DOE-0904874. IEEE Comput. This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. Benchmark for matrix multiplications between dense and block sparse (BSR) matrix in TVM, blocksparse (Gray et al.) 2013. 2005. Retrieved from http://www.itrs.net/Links/2005ITRS/AP2011.pdf. Sparse Matrix-Matrix Multiplication Benchmark Code for Intel Xeon and Xeon Phi This repository contains the benchmark code supplementing my blog post on a matrix-matrix multiplication benchmark on Intel Xeon and Xeon Phi. The microbenchmarking functions take the DenseMatrixMicrobenchmark object defining the microbenchmark and the list of data objects returned by the allocator function. http://people.sc.fsu.edu/~jburkardt/pdf/hbsmc.pdf (1992). Sparse Matrix Vector multiplication (SpMV) is an important kernel in both traditional high performance computing and emerging data-intensive applications. Check if you have access through your login credentials or your institution to get full access on this article. ACM Trans. http://doi.acm.org/10.1145/3127479.3131614, Yu, Y., Tang, M., Aref, W.G., Malluhi, Q.M., Abbas, M.M., Ouzzani, M.: In-memory distributed matrix computation processing and optimization. , is derived from the matrix on the fine grid, , 2007. U. W. Pooch and A. Nieder. For example, the MKL_NUM_THREADS environment variable should be set when the dense matrix benchmarks are tested using an instance of R that is linked to the parallel Intel Math Kernel Library which implements multithreaded BLAS and LAPACK functionality. It provides functionality that can be used to build GPU accelerated solvers. CSX: An extended compression format for SpMV on shared memory systems. http://ilpubs.stanford.edu:8090/422/, Park, J., Kim, , H., Lee, K.: Evaluating concurrent executions of multiple function-as-a-service runtimes with microvm. Standards 49, 6 (1952), 409--436. 2013. K. Kourtis, V. Karakasis, G. Goumas, and N. Koziris. Tax calculation will be finalised during checkout. We first characterize various distributed SPMM implementations on Apache Spark. R HPC Benchmark - The Comprehensive R Archive Network Furthermore, there are two types of autotuning characteristics facilitating the adaptation, both to the sparsity structure of the treated matrix and to the available hardware platform. Sparse Matrix-Matrix Multiplication Benchmark on Intel Xeon and Xeon Phi (KNC, KNL) from blog post: This repository contains the benchmark code supplementing my blog post on a matrix-matrix multiplication benchmark on Intel Xeon and Xeon Phi. OSDI04. Routines for sparse matrix x sparse matrix addition and multiplication, Generic high-performance APIs for sparse-dense vector multiplication (SpVV), sparse matrix-dense vector multiplication (SpMV), and sparse matrix-dense matrix multiplication (SpMM). Ali Pinar and Michael T. Heath. Cloud Comput. The online phase is for partitioning the input sparse matrix and computing the execution plan. The microbenchmark definition classes also have parameters used to specify dimensions of the data sets to be used with the microbenchmarks. Math. I. S. Duff and J. K. Reid. The paper then presents a performance analysis for the sparse matrix-vector multiplication for each of these three storage formats. : Practical Bayesian optimization of machine learning algorithms. On the left are the full matrix organized in blocks and its internal memory representation: compressed values and block indices. A. Ilic, F. Pratas, and L. Sousa. Association for Computing Machinery, New York, pp. This example runs all of the default machine learning microbenchmarks, saves the summary statistics for each microbenchmark in the directory MachineLearningResults, and saves the data frame returned from the dense matrix benchmark to a file named allResultsFrame.RData. https://doi.org/10.1109/tcc.2019.2950400, Klimovic, A., Litz, H., Kozyrakis, C.: Selecta: Heterogeneous cloud storage configuration for data analytics. Comput. This format allows fast row access and matrix-vector multiplications (Mx). Full suite of sparse routines covering sparse vector x dense vector operations, sparse matrix x dense vector operations, and sparse matrix x dense matrix operations. In Proceedings of the 1992 ACM/IEEE Conference on Supercomputing. The cuSPARSE library provides cusparseSpMM routine for SpMM operations. Exp. appears when forming the normal equations of interior point methods The novelty compared to previous benchmarks is that it is not limited by the need for a compiler. https://doi.org/10.3390/bdcc4040032, Foundation, A.S.: Apache hadoop (2004). A high performance algorithm using pre-processing for the sparse matrix-vector multiplication. cuSPARSE is widely used by engineers and scientists . Iterative Methods for Sparse Linear Systems (2nd ed.). International Conference on Parallel Processing and Applied Mathematics, PPAM 2013: Parallel Processing and Applied Mathematics Software 31(3), 351362 (2005), CrossRef The allocator must return a list of allocated data objects, including the matrix of feature vectors, for the microbenchmark to operate on. The Collection is widely used by the numerical linear algebra community for the development and performance evaluation of sparse matrix algorithms. The procedures for obtaining and using the test collection are discussed. either explicitly or implicitly, in very http://bebop.cs.berkeley.edu/smc/formats/matlab.html (2008). A survey of indexing techniques for sparse matrices. However, if the functionality being microbenchmarked is implemented with support for multithreading, and the number of threads can be controlled through the use of environment variables, as is often the case, then the benchmarks can be executed multithreaded. Samuel Williams, Andrew Waterman, and David Patterson. 759773 (2018). To extract the row 1 (the second row) of this matrix we set row_start=1 and row_end=2. T. Davis and Y. Hu. Google Scholar, Seo, S., Yoon, E.J., Kim, J., Jin, S., Kim, J., Maeng, S.: Hama: An efficient matrix computation with the mapreduce framework. 1996. PDF Design Principles for Sparse Matrix Multiplication on the GPU For consistent behavior, the user should set the environment variable for the number of threads before the R programming environment is initialized. Before running the examples, certain environment variables need to be set to specify the number of threads for parallel processing; see the previous section for how to properly do this. ACM, New York, Article 50. In Proceedings of ACM SIGGRAPH 2005 Courses (SIGGRAPH05). 2001. (ed.) Bur. The function GetDenseMatrixDefaultMicrobenchmarks defines the default microbenchmarks referenced in the table. http://ce.et.tudelft.nl/iliad/d-sab/, Computer Engineering Laboratory, Electrical Engineering Department, Delft University of Technology, 2600 GA, Delft, The Netherlands, Pyrrhos Stathis,Stamatis Vassiliadis&Sorin Cotofana, You can also search for this author in Methods of conjugate gradients for solving linear systems. (eds.) USENIX, San Jose, pp. PubMedGoogle Scholar, Novosibirsk State Technical University, Russia, Stathis, P., Vassiliadis, S., Cotofana, S. (2003). In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waniewski, J. Ideally, the entries are sorted first by row index and then by column index, to improve random access times. In Proceedings of the 1991 ACM/IEEE Conference on Supercomputing. Table 1 shows the supported data types, layouts, and compute types. Matrix-vector multiplications are very often one of the most time-consuming parts of the treatment. Provided by the Springer Nature SharedIt content-sharing initiative, Parallel Processing and Applied Mathematics, https://doi.org/10.1007/978-3-642-55224-3_18, http://people.sc.fsu.edu/~jburkardt/pdf/hbsmc.pdf, http://bebop.cs.berkeley.edu/smc/formats/matlab.html. 10471058 (2017), Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauly, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. Use 128-byte aligned pointers for matrices for vectorized memory access. J. D. McCalpin. Res. Sparse Matrix-Vector Multiplication PETSc oers a wide range of high-level components re-quired for linear algebra, such as linear and non-linear solversas well as preconditioners. Symposium on the Birth of Numerical Analysis. Next, the authors present their SparseX library. Reducing the bandwidth of sparse symmetric matrices. for large scale numerical optimization. A block-diagonal matrix A has the form. In: Proceedings of the 1988 International Conference on Supercomputing, St. Malo, France, pp. Birkhuser Press, 163--202. The indices array stores row indices, and each element of the indptr array . As another example, the following sparse matrix has lower and upper bandwidth both equal to 3. Basic linear algebra subprograms for Fortran usage. Correspondence to Compressed sparse row (CSR, CRS or Yale format), "Cerebras Systems Unveils the Industry's First Trillion Transistor Chip", "Argonne National Laboratory Deploys Cerebras CS-1, the World's Fastest Artificial Intelligence Computer | Argonne National Laboratory", "Sparse Matrix Multiplication Package (SMMP)", Oral history interview with Harry M. Markowitz, Jennifer Scott and Miroslav Tuma: "Algorithms for Sparse Linear Systems", Birkhauser, (2023), DOI: https://doi.org/10.1007/978-3-031-25820-6 (Open Access Book), "A comparison of several bandwidth and profile reduction algorithms", "Sparse matrices in MATLAB: Design and Implementation", Fundamental (linear differential equation), https://en.wikipedia.org/w/index.php?title=Sparse_matrix&oldid=1159120975, Creative Commons Attribution-ShareAlike License 4.0. Or does it make sense to use just in case? Improving the performance of the symmetric sparse matrix-vector multiplication in multicore. A benchmark of sparse matrix dense vector multiplication in C++ using homebuilt and pre-packaged methods. Benchmark of C++ Libraries for Sparse Matrix Computation Recognizing the adoption of manycore accelerators in HPC, we evaluate in this paper the performance of the currently best sparse matrix-vector product (SpMV) implementations on high-end GPUs from AMD and NVIDIA.Specifically, we optimize SpMV kernels for the CSR, COO, ELL, and . V. Karakasis, T. Gkountouvas, K. Kourtis, G. Goumas, and N. Koziris. PPAM 2013. Archit. Nonetheless, this does avoid the need to handle an exceptional case when computing the length of each row, as it guarantees the formula ROW_INDEX[i + 1] ROW_INDEX[i] works for any row i. Technical Report NISTIR-5935. Matrix in which most of the elements are zero, "sparsity" redirects here. V. H. F. Batista, G. O. Ainsworth, Jr., and F. L. B. Ribeiro. See the object documentation for GetDenseMatrixDefaultMicrobenchmarks for more details. The following are open-source: The term sparse matrix was possibly coined by Harry Markowitz who initiated some pioneering work but then left the field.[12]. Comput. SPEC CPU2006 benchmark descriptions. The old Yale format works exactly as described above, with three arrays; the new format combines ROW_INDEX and COL_INDEX into a single array and handles the diagonal of the matrix separately.[10]. MATH The authors of the paper are trying to a) provide simple and clear semantics; b) serve users with different levels of expertise; c) facilitate the integration of their kernels in large-scale sparse solver libraries; and d) provide transparent adaptation to the available target platform. 1979. By contrast, if most of the elements are non-zero, the matrix is considered dense. for large sparse linear systems, the matrix on a coarse grid, It is similar to COO, but compresses the row indices, hence the name. Math. We acknowledge the Louisiana Optical Network Initiative (LONI) for providing HPC resources. It is especially prevalent initerative methods to solve linear systems. Using cuSPARSE, applications automatically benefit from regular performance improvements and new GPU architectures. Park, J., Lee, K. S-MPEC: Sparse Matrix Multiplication Performance Estimator on a Cloud Environment. In this context, the sparse matrix-matrix multiplication is of special interest. Blending extensibility and performance in dense and sparse parallel data management. For better performance, it is important to satisfy the following conditions: For this new storage format, perform similar steps as with CSR and COO cusparseSpMM. Specialized computers have been made for sparse matrices,[2] as they are common in the machine learning field. Software 38 (2011), 1--25. http://www.cise.ufl.edu/research/sparse/matrices. The code is provided under a permissive MIT/X11-style license. A tag already exists with the provided branch name. SIAM J. Sci. Depending on the number and distribution of the non-zero entries, different data structures can be used and yield huge savings in memory when compared to the basic approach. scheme is used. Software 31, 3 (2005), 397--423. This example runs all but the matrix transpose microbenchmarks, which tend to run very slowly, and saves the results to the same directory as in the previous example. Storing a sparse matrix. Are you sure you want to create this branch? Sparse Linear Algebra on AMD and NVIDIA GPUs - The Race Is On - Springer The integer index is unused by the microbenchmarks specified by the GetSparse* default functions because the sparse matrix microbenchmarks read the test matrices from files as opposed to dynamically generating them. Michael A. Heroux, Roscoe A. Bartlett, Vicki E. Howle, Robert J. Hoekstra, Jonathan J. Hu, Tamara G. Kolda, Richard B. Lehoucq, Kevin R. Long, Roger P. Pawlowski, Eric T. Phipps, Andrew G. Salinger, Heidi K. Thornquist, Ray S. Tuminaro, James M. Willenbring, Alan Williams, and Kendall S. Stanley. Sparse Matrix; Sparse . We develop simple models for estimating the achievable maximum D. Lukarski. 1992. Pattern-based sparse matrix representation for memory-efficient SMVM kernels. 2003. https://doi.org/10.1007/978-3-642-55224-3_18, DOI: https://doi.org/10.1007/978-3-642-55224-3_18, Publisher Name: Springer, Berlin, Heidelberg, eBook Packages: Computer ScienceComputer Science (R0). Learn. Those that support efficient access and matrix operations, such as CSR (Compressed Sparse Row) or CSC (Compressed Sparse Column). All rows in the arrays must have the same number of blocks. 2001. The pam function implements the partitioning around medoids algorithm which has quadratic time complexity. In: 2015 IEEE International Conference on Big Data (Big Data), October 2015, pp. This operation A discussion of three storage formats for sparse matrices follows: a) the compressed sparse row (CSR) format, b) the blocked compressed sparse row (BCSR) format, and c) the CSX format. ACM, New York, 87--96. Math. To overcome this limitation, the NVIDIA Ampere architecture introduces the concept of fine-grained structured sparsity, which doubles throughput of dense-matrix multiplies by skipping the computation of zero values in a 2:4 pattern. Correspondence to Execution environments of distributed SPMM tasks on cloud resources can be set up in diverse ways with respect to the input sparse datasets, distinct SPMM implementation methods, and the choice of cloud instance types. D. H. Bailey, E. Barscz, J. T. Barton, D. S. Browning, R. L. Carter, L. Dagum, R. A. Fatoohi, P. O. Frederickson, T. A. Lasinksi, R. S. Schreiber, H. D. Simon, V. Venkatakrishnan, and S. K. Weeratunga. Y. Saad. In: 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17). A M. Martone, S. Filippone, S. Tucci, M. Paprzycki, and M. Ganzha. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Accelerating Matrix Multiplication with Block Sparse Format and NVIDIA Thus, the environment variable specifying the number of threads must be retrievable in a way that is portable regardless of which multithreaded library the R programming environment is linked with. Tilmann Gneiting and Adrian E. Raftery. 2006. (TPDS) 24, 10 (2013), 1930--1940. This example shows how to specify a new clustering microbenchmark and run it. We developed the RHPCBenchmark package to determine the single-node run time performance of compute intensive linear algebra kernels that are common to many data analytics algorithms, and the run time performance of machine learning functionality commonly implemented with linear algebra operations. Tech. Kornilios Kourtis, Georgios Goumas, and Nectarios Koziris. Performance considerations with sparse matrices in Armadillo - Rcpp Technical Report 4744. 38(1), 1:11:25 (2011), MathSciNet Each microbenchmark definition class has an allocator function field and a benchmark function field which specify functions for allocating the data for the microbenchmark and the function that performs the timing of functionality being tested. GitHub - jamesETsmith/SpMV_benchmark: A benchmark of sparse matrix A unified sparse matrix data format for efficient general sparse matrix-vector multiply on modern processors with wide SIMD units. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC14). The R data files containing the sparse matrices can be downloaded in the companion package RHPCBenchmarkData. Denote the layouts of the matrix B with N for row-major order, where op is non-transposed, and T for column-major order, where op is transposed. Retrieved from http://www.cs.virginia.edu/stream/. GitHub - karlrupp/spgemm-mkl-benchmark: Sparse Matrix-Matrix CSC is similar to CSR except that values are read first by column, a row index is stored for each value, and column pointers are stored. If any of the microbenchmarks fails to run in a timely manner or fails due to memory constraints, the matrix sizes and number of performance trials per matrix can be adjusted. This is a preview of subscription content, access via your institution. The microbenchmarks, their associated identifiers and brief descriptions of the tested matrices are given in the table below. Its optimization, which is intimately associated with the data structures used to store the sparse matrix, has always been of particular interest to the applied mathematics and computer science communities and has attracted further attention since the advent of multicore architectures. As the usual dense GEMM, the computation partitions the output matrix into tiles. MATH Technical report SAND2003-2927, Sandia National Laboratories (2003), Hoemmen, M.: Matlab (ASCII) sparse matrix format, berkeley Benchmarking and Optimization Group. Jiajia Li, Guangming Tan, Mingyu Chen, and Ninghui Sun. Jan Treibig, Georg Hager, and Gerhard Wellein. 6, ser. (ed.) USENIX Association, Boston, July 2018, pp. Nat. In: Proceedings of the 11th International Conference on Supercomputing, ser. Optimization of the R interpreter, its intrinsic functionality, and R packages for specific hardware architectures will be necessary for data analysts to take full advantage of the latest HPC clusters, and to obviate the need to reengineer their analysis workflows. See file LICENSE.txt for details. Eun-Jin Im and Katherine Yelick. Google Scholar, Saad, Y.: Iterative Methods for Sparse Linear Systems, 2nd edn. Lett. World Scientific Press, Singapore (2009), Cheng, Y., Iqbal, M.S., Gupta, A., Butt, A.R. https://doi.org/10.1007/s10586-021-03287-3, DOI: https://doi.org/10.1007/s10586-021-03287-3. Lecture Notes in Computer Science, vol 2763. In deep learning, block sparse matrix multiplication is successfully adopted to reduce the complexity of the standard self-attention mechanism, such as in Sparse Transformer models or in its extensions like Longformer. : FC2: cloud-based cluster provisioning for distributed machine learning. Sparse matrix - Wikipedia Modern Mathematical Models, Methods and Algorithms for Real World Systems, 420--447. Iterative methods, such as conjugate gradient method and GMRES utilize fast computations of matrix-vector products ACM, New York, 117--126. All of the dense linear algebra kernels are implemented around BLAS or LAPACK interfaces. It provides functionality that can be used to build GPU accelerated solvers. In: Presented as part of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12). Google Scholar, Heroux, M., Bartlett, R., Hoekstra, V.H.R., Hu, J., Kolda, T., Lehoucq, R., Long, K., Pawlowski, R., Phipps, E., Salinger, A., Thornquist, H., Tuminaro, R., Willenbring, J., Williams, A.: An overview of trilinos. : Performance of various computers using standard linear equations software in a Fortran environment. This is a preview of subscription content, access via Comput. Sandia National Laboratories. Benchmark for matrix multiplications between dense and block sparse (BSR) matrix in TVM, blocksparse (Gray et al.) Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 2015. 2005. M. A. Heroux, D. W. Doerfler, P. S. Crozier, J. M. Willenbring, H. C. Edwards, A. Willians, M. Rajan, E. R. Keiter, H. K. Thornquist, and R. W. Numrich. IEEE Trans. OCI-0904874, and by the U.S. Department of Energy under Grant No. The International Journal of Supercomputer Applications5(3), 6373 (1991), CrossRef http://doi.acm.org/10.1145/2391229.2391239, Kepner, J., Gilbert, J.: Graph Algorithms in the Language of Linear Algebra. You signed in with another tab or window. Performance evaluation of the sparse matrix-vector multiplication on modern architectures. It lists several relationships and explains that they can easily be used to evaluate the expected performance of the sparse matrix-vector multiplication for each of the three formats. The resulting matrices are distributed among processors of a parallel computer system. and cuSparse. C. L. Lawson, R. J. Hanson, D. R. Kincaid, and F. T. Korgh. ACM, New York, 157--172. SparseBench home page - Netlib 2013. IEEE Proc. Copyright 2023 ACM, Inc. ACM Transactions on Mathematical Software. https://doi.org/10.1137/1.9780898719918, Kim, J., Lee, K.: Functionbench: a suite of workloads for serverless cloud function service. This repository contains benchmarking results for different ways to extract diagonal entries from a sparse matrix in PyTorch. 469482 (2017). 2010, 721726 (2010), Shahidinejad, A., Ghobaei-Arani, M., Masdari, M.: Resource provisioning using workload clustering in cloud computing environment: a hybrid approach. 1997. 77(4), 802813 (2008), Foldi, T., von Csefalvay, C., Perez, N.A. The two phases of our solution for the sparse matrix partitioning problem for SpMV on CPU-GPU heterogeneous platforms. Stanford InfoLab, Technical Report 1999-66, November 1999, previous number = SIDL-WP-1999-0120 (1999). Please try again. Daniel Langr . cuSPARSE is widely used by engineers and scientists working on applications such as machine learning, computational fluid dynamics, seismic exploration and computational sciences. In: NSDI, pp. Starting with cuSPARSE 11.4.0, the CUDA Toolkit provides a new high-performance block sparse matrix multiplication routine that allows exploiting NVIDIA GPU dense Tensor Cores for nonzero sub-matrices and significantly outperforms dense computations on Volta and newer architecture GPUs. In Proceedings of the ISCA 25th International Conference on Computers and Their Applications (CATA10). Intel Math Kernel Library. It is likely known as the Yale format because it was proposed in the 1977 Yale Sparse Matrix Package report from Department of Computer Science at Yale University.[11]. These are based on a suite of par-allel data structures which implement basic vector and matrix June 8, 2021 operations. Alipourfard, O., Liu, H.H., Chen, J., Venkataraman, S., Yu, M., Zhang, M.: Cherrypick: adaptively unearthing the best cloud configurations for big data analytics. 21, 06 (2018), Snoek, J., Larochelle, H., Adams, R.P. Each returns a vector of SparseMatrixMicrobenchmark objects specifying each microbenchmark. Several figures and tables illustrate the performance of the tools developed for sparse matrix-vector multiplication. Accessed 20 Nov 2017, van de Geijn, R.A., Watts, J.: Summa: Scalable universal matrix multiplication algorithm. The ACM Digital Library is published by the Association for Computing Machinery. Recently, NVIDIA introduced the cuSPARSELt library to fully exploit third-generation Sparse Tensor Core capabilities. : Greedy function approximation: a gradient boosting machine. 4556 (2015). Structured Sparsity in the NVIDIA Ampere Architecture and Applications in Search Engines, Accelerating ReLu and GeLu Activation Functions, and Batched Sparse GEMM in cuSPARSELt v0.2.0, cuSPARSELt v0.1.0 Now Available: Arm and Windows Support, Exploiting NVIDIA Ampere Structured Sparsity with cuSPARSELt, Improving GPU Performance by Reducing Instruction Cache Misses, CUDA 12.1 Supports Large Kernel Parameters, Harnessing the Power of NVIDIA AI Enterprise on Azure Machine Learning, Webinar: Performant Multiphase Flow Simulation at Leadership-Class Scale, Asynchronous Error Reporting: When printf Just Wont Do, Generating Long Sequences with Sparse Transformers, Accelerating CUDA C++ Applications with Multiple GPUs, Recent Developments in NVIDIA Math Libraries (Spring 2023), Developing Optimal CUDA Kernels on Hopper Tensor Cores (Spring 2023).
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