1. Dependence Analysis Important and difficult
Parallelize
Optimize memory hierarchy
Loop --- and non-loops ?
Application to program transformation
Should yield “better” code
Should NOT alter code semantics
atof( ) - converts the "character string" pointed to by nptr to a
double and returns its value
atol( ) - converts the "character string" pointed to by str to a long
and returns its value
atoi( ) - converts the "character string" pointed to by str to an int

2. Which Loop(s) Parallelizable ?1)
Do I = 1, n
A[I] = 5 * B[I] + A[I]
Enddo 2)
Do I = 1, n
A[I-1] = 5 * B[I] + A[I]
Enddo 3)
Do I = 1, n
temp = 5 * B[I]
A[I] = temp
Enddo 4)
Do I = 1, n
A[I+1] = 5 * B[I] + A[I]
Enddo

3. Data Dependence Statement S2 is dependent on S1 iff Dependence “types”
S2 follows S1 in execution
S2, S1 access same memory location, M
One of S2 or S1 write to M
Dependence “types”
True (or flow) -- S2 reads M set by S1
Anti- -- S2 writes M read by S1
Output -- S1 and S2 write M
Dependence Graph

4. Example
S1: a = 3.14
S2: b = 5.0
S3: c = a * b * b S1 S2 S3

5. Example S1: a = 3.14 S1 S2 S2: b = 5.0 S3: c = a * b * b S4: b = a * c

6. Program Order, Dependence
Sequential order of imperative programs is too restrictive
Partial order of dependences needed to ensure “correctness”
Reordering transformations should not violate dependence
Can we always determine dependence?

7. Dependence in Loops Parameterize stmts by loop iterations
Loop nesting level: number of surrounding loops + 1
Iteration number: value of iteration (index) variable
Iteration vector, I, of loop is the value of all index variables for outer loop(s) for a particular iteration of innermost loop
Iteration space is set of all possible iteration vectors for a statement