Dynamically Scheduled HLS: Program Analysis and Control Flow

Related lectures (32)

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Covers the basics of Python programming, focusing on flow control structures, interactive mode, script mode, and variables.

Explores static analysis techniques like AST-based analysis and taint analysis applied to the Amy language.

Covers types of errors in programming and emphasizes the importance of debugging and optimization techniques.

Explores closure conversion, function hoisting, and dataflow analysis for program optimization.

Explores the ingredients and selection of algorithms for different goals.

Covers program optimizations, abstract interpretation, algebraic transformations, and interval analyses.

Covers the efficiency of register machines over stack machines, memory organization, and mapping instructions.

Explores managing large codebases, higher abstraction levels, CodeDB, Datalog queries, and program analysis.

Covers the Compiler Extension Lab, focusing on adding new functionality to a simple functional language compiler.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.