Appendix G - Clemson University

G-52 ■ Appendix G Vector Processors
tional units and the increased complexity of assigning operations to units, all the
overheads (T and T ) are doubled.
loop start
a. [15] Find the number of clock cycles for code sequence 1 on
VMIPS.
b. [20] Find the number of clock cycles on code sequence 1 for
VMIPS-II. How does this compare to VMIPS?
c. [15] Find the number of clock cycles on code sequence 2 for
VMIPS.
d. [15] Find the number of clock cycles on code sequence 2 for
VMIPS-II. How does this compare to VMIPS?
G.9 [20] Here is a tricky piece of code with two-dimensional arrays. Does this
loop have dependences? Can these loops be written so they are parallel? If so,
how? Rewrite the source code so that it is clear that the loop can be vectorized, if
possible.
do 290 j = 2,n
do 290 i = 2,j
aa(i,j)= aa(i-1,j)*aa(i-1,j)+bb(i,j)
290 continue
G.10 [12/15] Consider the following loop:
do 10 i = 2,n
A(i) = B
10 C(i) = A(i-1)
a. [12] Show there is a loop-carried dependence in this code fragment.
b. [15] Rewrite the code in FORTRAN so that it can be vectorized as two
separate vector sequences.
G.11 [15/25/25] As we saw in Section G.5, some loop structures are not easily
vectorized. One common structure is a reduction—a loop that reduces an array to
a single value by repeated application of an operation. This is a special case of a
recurrence. A common example occurs in dot product:
dot = 0.0
do 10 i=1,64
10 dot = dot + A(i) * B(i)
This loop has an obvious loop-carried dependence (on dot) and cannot be vectorized
in a straightforward fashion. The first thing a good vectorizing compiler
would do is split the loop to separate out the vectorizable portion and the recurrence
and perhaps rewrite the loop as
do 10 i=1,64
10 dot(i) = A(i) * B(i)
do 20 i=2,64
20 dot(1) = dot(1) + dot(i)