Code scheduling is a form of program optimization that reorders operations in machine code while satisfying three main types of constraints: data dependence constraints to preserve program semantics, resource constraints to avoid oversubscribing machine resources, and true/anti dependencies related to reads and writes of the same memory location. Data dependence analysis is needed but is generally undecidable at compile time so the compiler must conservatively assume operations may refer to the same location. Register allocation and code scheduling affect each other, so scheduling is often done before or after allocation to minimize storage dependences. Scheduling within basic blocks is relatively easy but blocks tend to be small with little parallelism. Memory loads in particular can benefit from speculative execution.

1. Code Scheduling
Constraints
P.Abinaya
Msc(cs)

2.  Code scheduling is a form of program optimization
that applies to the machine code that is produced by
the code generator. Code scheduling is subject to three
kinds of constraints:
 Data-dependence constraints. The operations in the
optimized program must produce the same results as
the corresponding ones in the original program.
 Resource constraints. The schedule must not
oversubscribe the resources on the machine.

3.  True dependence: read after write. If a write is
followed by a read of the same location, the read
depends on the value written; such a dependence
is known as a true
 Antidependence: write after read. If a read is
followed by a write to the same location, we say
that there is an antidependence from the read to
the write.

4.  To check if two memory accesses share a data
dependence, we only need to tell if they can refer to
the same location; we do not need to know which
location is being accessed.
 For example, we can tell that the two accesses *p and
O p ) + 4 cannot refer to the same location, even
though we may not know what p points to. Data
dependence is generally undecidable at compile time.
The compiler must assume that operations may refer
to the same location unless it can prove otherwise.

5.  machine-independent intermediate rep-resentation of
the source program uses an unbounded number of
pseudoregisters to represent variables that can be
allocated to registers. These variables include scalar
variables in the source program that cannot be
referred to by any other names, as well as temporary
variables that are generated by the compiler to hold
the partial results in expressions. Unlike memory
locations, registers are uniquely named. Thus precise
data-dependence constraints can be generated for
register accesses easily.

6.  If registers are allocated before scheduling, the
resulting code tends to have many storage
dependences that limit code scheduling. On the other
hand, if code is scheduled before register allocation,

7.  Scheduling operations within a basic block is relatively
easy because all the instructions are guaranteed to
execute once control flow reaches the beginning of the
block. Instructions in a basic block can be reordered
arbitrarily, as long as all the data dependences are
satisfied. Unfortunately, basic blocks,
 especially in nonnumeric programs, are typically very
small; on average,
 there are only about five instructions in a basic block.
In addition, operations in the same block are often
highly related and thus have little parallelism.

8.  Memory loads are one type of instruction that can
benefit greatly from specula-tive execution. Memory
loads are quite common, of course.
 They have relatively long execution latencies,
addresses used in the loads are commonly available in
advance, and the result can be stored in a new
temporary variable without destroying the value of any
other variable.

9.  Many machines can be represented using the
following simple model. A machine M = {R,T), consists
of
 :A set of operation types T, such as loads, stores,
arithmetic operations, and so on.
 A vector R = [n, r2,... ] representing hardware
resources, where r« is the number of units available of
the ith kind of resource. Examples of typical resource
types include: memory access units, ALU's, and
floating-point functional units.