37 citations · 38 across the 9 of their papers we have counts for
9 papers
ARASH: Adaptive Retrieval And Shot Selection for Tabular Prediction
Samirasadat Jamalidinan, Yue Xu, Kazem Cheshmi
Tabular prediction is a critical task across numerous applications. The recent success of large language models has sparked various approaches for adapting them to the tabular doma…
A Novel Compiler Transformation for Fast Sparse Matrix Multiplication in GPUs
Hossein Albakri, Kazem Cheshmi
Sparse data structures are commonly used in neural networks to reduce the memory footprint. These data structures are compact but cause irregularities such as random memory accesse…
Floating-Point Data Transformation for Lossless Compression
Samirasadat Jamalidinan, Kazem Cheshmi
Floating-point data is widely used across various domains. Depending on the required precision, each floating-point value can occupy several bytes. Lossless storage of this informa…
Improving Locality in Sparse and Dense Matrix Multiplications
Mohammad Mahdi Salehi Dezfuli, Kazem Cheshmi
Consecutive matrix multiplications are commonly used in graph neural networks and sparse linear solvers. These operations frequently access the same matrices for both reading and w…
Vectorizing Sparse Matrix Codes with Dependency Driven Trace Analysis
Zachary Cetinic, Kazem Cheshmi, Maryam Mehri Dehnavi
Sparse computations frequently appear in scientific simulations and the performance of these simulations rely heavily on the optimization of the sparse codes. The compact data stru…
Composing Loop-carried Dependence with Other Loops
Kazem Cheshmi, Michelle Mills Strout, Maryam Mehri Dehnavi
Sparse fusion is a compile-time loop transformation and runtime scheduling implemented as a domain-specific code generator. Sparse fusion generates efficient parallel code for the…
MatRox: Modular approach for improving data locality in Hierarchical (Mat)rix App(Rox)imation
Bangtian Liu, Kazem Cheshmi, Saeed Soori +2
Hierarchical matrix approximations have gained significant traction in the machine learning and scientific community as they exploit available low-rank structures in kernel methods…
Sparse Matrix Code Dependence Analysis Simplification at Compile Time
Mahdi Soltan Mohammadi, Kazem Cheshmi, Ganesh Gopalakrishnan +5
Analyzing array-based computations to determine data dependences is useful for many applications including automatic parallelization, race detection, computation and communication…
Sympiler: Transforming Sparse Matrix Codes by Decoupling Symbolic Analysis
Kazem Cheshmi, Shoaib Kamil, Michelle Mills Strout +1
Sympiler is a domain-specific code generator that optimizes sparse matrix computations by decoupling the symbolic analysis phase from the numerical manipulation stage in sparse cod…