{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/162938"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/162938","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Improving the Programmability of A Distributed Hardware Accelerator","abstract":"Sparse iterative matrix algorithms are critical to many scientific and engineering workloads, yet they perform poorly on conventional hardware. (Ōmeteōtl, a new hardware accelerator with a distributed-memory and task-based execution model, aims to address these performance bottlenecks. However, programming for (Ōmeteōtl is low-level, error-prone, and far removed from the simplicity of typical iterative formulations. This thesis presents Lapis, a domain-specific language and compiler that allows users to express sparse matrix algorithms in high-level Python code and automatically generates efficient C++ code for (Ōmeteōtl. Lapis abstracts away data partitioning and task orchestration, reducing implementation complexity: for example, it lowers lines of code by 30× for conjugate gradients and 46× for power iteration. Despite this abstraction, generated code achieves 75.7% to 92.6% of the performance of manually written implementations across several benchmarks.","abstract_html":"Sparse iterative matrix algorithms are critical to many scientific and engineering workloads, yet they perform poorly on conventional hardware. (Ōmeteōtl, a new hardware accelerator with a distributed-memory and task-based execution model, aims to address these performance bottlenecks. However, programming for (Ōmeteōtl is low-level, error-prone, and far removed from the simplicity of typical iterative formulations. This thesis presents Lapis, a domain-specific language and compiler that allows users to express sparse matrix algorithms in high-level Python code and automatically generates efficient C++ code for (Ōmeteōtl. Lapis abstracts away data partitioning and task orchestration, reducing implementation complexity: for example, it lowers lines of code by 30× for conjugate gradients and 46× for power iteration. Despite this abstraction, generated code achieves 75.7% to 92.6% of the performance of manually written implementations across several benchmarks.","abstract_has_math":false,"creators":["Shwatal, Nathan A."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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