In computer science, instruction scheduling is a compiler optimization used to improve instruction-level parallelism, which improves performance on machines with instruction pipelines. Put more simply, it tries to do the following without changing the meaning of the code: Avoid pipeline stalls by rearranging the order of instructions. Avoid illegal or semantically ambiguous operations (typically involving subtle instruction pipeline timing issues or non-interlocked resources). The pipeline stalls can be caused by structural hazards (processor resource limit), data hazards (output of one instruction needed by another instruction) and control hazards (branching). Data hazards Instruction scheduling is typically done on a single basic block. In order to determine whether rearranging the block's instructions in a certain way preserves the behavior of that block, we need the concept of a data dependency. There are three types of dependencies, which also happen to be the three data hazards: Read after Write (RAW or "True"): Instruction 1 writes a value used later by Instruction 2. Instruction 1 must come first, or Instruction 2 will read the old value instead of the new. Write after Read (WAR or "Anti"): Instruction 1 reads a location that is later overwritten by Instruction 2. Instruction 1 must come first, or it will read the new value instead of the old. Write after Write (WAW or "Output"): Two instructions both write the same location. They must occur in their original order. Technically, there is a fourth type, Read after Read (RAR or "Input"): Both instructions read the same location. Input dependence does not constrain the execution order of two statements, but it is useful in scalar replacement of array elements. To make sure we respect the three types of dependencies, we construct a dependency graph, which is a directed graph where each vertex is an instruction and there is an edge from I1 to I2 if I1 must come before I2 due to a dependency. If loop-carried dependencies are left out, the dependency graph is a directed acyclic graph. Then, any topological sort of this graph is a valid instruction schedule. The edges of the graph are usually labelled with the latency of the dependence. This is the number of clock cycles that needs to elapse before the pipeline can proceed with the target instruction without stalling. Algorithms The simplest algorithm to find a topological sort is frequently used and is known as list scheduling. Conceptually, it repeatedly selects a source