Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage | Paper Citation Record

Observation 48a63018-7cf9-4643-95a2-162f8bdf7e07 · outbound

This paper cites https://docs.nvidia.com/cuda/cublas/index.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage https://docs.nvidia.com/cuda/cublas/index

Reference 1

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This paper cites M., Buluç, A., Williams, S., and Y ang, C.Optimizing sparse matrix- multiple vectors multiplication for nuclear configuration interaction calculations.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage M., Buluç, A., Williams, S., and Y ang, C.Optimizing sparse matrix- multiple vectors multiplication for nuclear configuration interaction calculations

Reference 2

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Observation c97a557a-fac1-401c-aa9a-0df47fb8909c · outbound

This paper cites Fast sparse matrix-vector multiplication on gpus for graph applications.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Fast sparse matrix-vector multiplication on gpus for graph applications

Reference 3

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Observation 490c7c82-b1d0-472b-95b2-eeab2e50fd88 · outbound

This paper cites Efficient sparse matrix-vector multiplication on cuda.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Efficient sparse matrix-vector multiplication on cuda

Reference 4

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This paper cites On the relations between ilus and factored approx- imate inverses.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage On the relations between ilus and factored approx- imate inverses

Reference 5

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Observation cde669b4-9605-4140-bbfd-e8e2ff075da4 · outbound

This paper cites In SC22: International Conference for High Performance Computing, Networking, Storage and Analysis (2022), IEEE, pp.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage In SC22: International Conference for High Performance Computing, Networking, Storage and Analysis (2022), IEEE, pp

Reference 6

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This paper cites Scaling algorithms for weighted matching in general graphs.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Scaling algorithms for weighted matching in general graphs

Reference 7

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Observation ba33c097-54d3-4f54-97a3-a3bb9d37a992 · outbound

This paper cites Spinfer: Leveraging low-level sparsity for efficient large language model inference on gpus.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Spinfer: Leveraging low-level sparsity for efficient large language model inference on gpus

Reference 8

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Observation 02d83cfa-cdc3-4bb5-b99a-fb6a4782ba62 · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 9

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This paper cites Leveraging index compression techniques to optimize the use of co-processors.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Leveraging index compression techniques to optimize the use of co-processors

Reference 10

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Observation f04e6b05-ae1f-4b3c-b175-a542ff16c034 · outbound

This paper cites Sparse GPU kernels for deep learning.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Sparse GPU kernels for deep learning

Reference 11

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Observation ced03fe2-8687-4b8a-beb0-a7f9fcee5273 · outbound

This paper cites ggerganov/llama.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage ggerganov/llama

Reference 12

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Observation 57e6c840-e4e6-4e2b-a172-e08de5a78e03 · outbound

This paper cites L., and Daga, M.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage L., and Daga, M

Reference 13

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Observation 8b4ff62c-ddbe-4048-ba7d-d7c0d0eab7c5 · outbound

This paper cites Y., Leng, J., Qiu, Y., Guan, Y., W ang, Z., Jia, X., Li, X., Guo, M., and Zhu, Y.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Y., Leng, J., Qiu, Y., Guan, Y., W ang, Z., Jia, X., Li, X., Guo, M., and Zhu, Y

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:57:37.018716Z

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Observation 6f701053-2b7c-44d3-849d-4b4e92d299a4 · outbound

This paper cites In Proceedings of the 24th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2019, Washington, DC, USA, February 16-20, 2019 (2019), ACM, pp.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage In Proceedings of the 24th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2019, Washington, DC, USA, February 16-20, 2019 (2019), ACM, pp

Reference 15

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Observation 241a9bab-12fb-49a2-a192-1c1353043314 · outbound

This paper cites Flashdecoding++: Faster large language model inference with asynchronization, flat gemm optimization, and heuristics.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Flashdecoding++: Faster large language model inference with asynchronization, flat gemm optimization, and heuristics

Reference 16

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Observation 66d04aa1-90c5-4445-b1c0-22621663ff4f · outbound

This paper cites In Proceedings of the 25th ACM SIGPLAN symposium on principles and practice of parallel program- ming (2020), pp.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage In Proceedings of the 25th ACM SIGPLAN symposium on principles and practice of parallel program- ming (2020), pp

Reference 17

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Observation ba390c32-a1f8-4c43-b859-fd9fa46e476a · outbound

This paper cites Maximum bounded 3-dimensional matching is max snp-complete.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Maximum bounded 3-dimensional matching is max snp-complete

Reference 18

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Observation c2a7b3a5-33ed-44cb-b088-a48c1f71cee7 · outbound

This paper cites Computational complexity of the perfect matching problem in hypergraphs with subcritical density.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Computational complexity of the perfect matching problem in hypergraphs with subcritical density

Reference 19

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This paper cites Optimizing sparse matrix-vector multiplication using index and value compression.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Optimizing sparse matrix-vector multiplication using index and value compression

Reference 20

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This paper cites ACM Transactions on Architecture and Code Optimization (2024).

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage ACM Transactions on Architecture and Code Optimization (2024)

Reference 21

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This paper cites Csr5: An efficient storage format for cross-platform sparse matrix-vector multiplication.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Csr5: An efficient storage format for cross-platform sparse matrix-vector multiplication

Reference 22

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Observation 9653f73c-a23b-4de5-9b01-490916a57d2e · outbound

This paper cites Spp: Sparsity-preserved parameter-efficient fine-tuning for large language models, 2024.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Spp: Sparsity-preserved parameter-efficient fine-tuning for large language models, 2024

Reference 23

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This paper cites Dasp: Specific dense matrix multiply-accumulate units accelerated general sparse matrix-vector multiplication.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Dasp: Specific dense matrix multiply-accumulate units accelerated general sparse matrix-vector multiplication

Reference 24

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This paper cites Llm-rec: Personalized recommendation via prompting large language models.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Llm-rec: Personalized recommendation via prompting large language models

Reference 25

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Observation 147c739a-f584-4fc4-a0ae-2abc12432e22 · outbound

This paper cites Advances in neural information processing systems 36 (2023), 21702–21720.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Advances in neural information processing systems 36 (2023), 21702–21720

Reference 26

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This paper cites Adell: An adaptive warp-balancing ell format for efficient sparse matrix-vector multiplication on gpus.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Adell: An adaptive warp-balancing ell format for efficient sparse matrix-vector multiplication on gpus

Reference 27

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This paper cites Merge-based parallel sparse matrix-vector multi- plication.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Merge-based parallel sparse matrix-vector multi- plication

Reference 28

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This paper cites In GPU Technology Conference (2010), vol.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage In GPU Technology Conference (2010), vol

Reference 29

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This paper cites In2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (2021), IEEE, pp.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage In2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (2021), IEEE, pp

Reference 30

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This paper cites PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU

Reference 31

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This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage A Simple and Effective Pruning Approach for Large Language Models

Reference 32

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This paper cites In 2011 International conference on parallel processing (2011), IEEE, pp.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage In 2011 International conference on parallel processing (2011), IEEE, pp

Reference 33

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Observation ebc2f897-3a61-4a56-b034-62f6b6b6b329 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 34

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Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Unresolved cited work

Reference 35

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This paper cites W., and Yelick, K.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage W., and Yelick, K

Reference 36

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This paper cites PrivateLoRA For Efficient Privacy Preserving LLM.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage PrivateLoRA For Efficient Privacy Preserving LLM

Reference 37

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This paper cites M.Register tiling for unstructured sparsity in neural network inference.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage M.Register tiling for unstructured sparsity in neural network inference

Reference 38

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This paper cites Accelerating sparse matrix computations via data compression.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Accelerating sparse matrix computations via data compression

Reference 39

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Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Unresolved cited work

Reference 40

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This paper cites Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 41

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This paper cites Besa: Pruning large language models with blockwise parameter- efficient sparsity allocation.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Besa: Pruning large language models with blockwise parameter- efficient sparsity allocation

Reference 42

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This paper cites A survey on large language model (llm) security and privacy: The good, the bad, and the ugly.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage A survey on large language model (llm) security and privacy: The good, the bad, and the ugly

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:57:34.233921Z

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This paper cites OPT: Open Pre-trained Transformer Language Models.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage OPT: Open Pre-trained Transformer Language Models

Reference 44

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This paper cites Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

Reference 45

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This paper cites Acc-spmm: Accelerating general-purpose sparse matrix-matrix multiplication with gpu tensor cores.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Acc-spmm: Accelerating general-purpose sparse matrix-matrix multiplication with gpu tensor cores

Reference 46

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Observation 2e83db51-a3e4-45a8-b62b-8ff6ef5584e5 · outbound

This paper cites A Survey of Large Language Models.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage A Survey of Large Language Models

Reference 47

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