1. 4- Performance Analysis of Parallel Programs

2. Performance Evaluation of Computer Systems
CPU time (response time metric):
Depends on the program and compiler efficiency.

3. Performance Evaluation of Computer Systems
2. MIPS and MFLOPS (throughput metric): depends on the program and type of instructions, no differentiation between floating point add and divide!

4. Performance Evaluation of Computer Systems
3. Performance of Processors with a Memory Hierarchy:
In case of multiple cache levels:

5. Performance Evaluation of Computer Systems
4. Benchmark Programs:
Synthetic benchmarks: small artificial programs that represent a large class of real applications. Such as Whetstone, Dhrystone
Kernel benchmarks: small but relevant parts of real applications, such as Livermore Loops, or toy programs like quicksort.
Real application benchmarks: several entire programs which reflect a workload of a standard user, called benchmark suites. Such as SPEC benchmarks (System Performance Evaluation Cooperation), and EEMBC benchmarks (EDV Embedded Microprocessor Benchmark Consortium).

6. Performance Metrics for Parallel Programs
parallel runtime Tp(n): the time between the start of the program and the end of the execution on all participating processors;
execution of local computations of each participating processor;
exchange of data between processors of a distributed address space;
synchronization of the participating processors when accessing shared data structures;
waiting times occurring because of an unequal load distribution;
Parallelization overhead

7. Performance Metrics for Parallel Programs
Cost of a parallel program Cp(n): total amount of work performed by all processors;
A parallel program is called cost-optimal if
Speedup:
Efficiency:

8. Speedup limit Amdahl’s law:
When a (constant) fraction f , 0 ≤ f ≤ 1, of a parallel program must be executed sequentially,
if 20% of a program must be executed sequentially, then the attainable speedup is limited to 1/f = 5.

9. Scalability
For a fixed problem size n a saturation of the speedup can be observed when the number p of processors is increased.
Efficiency can be kept constant if both the number p of processors and the problem size n are increased.
Larger problems can be solved in the same time as smaller problems if a sufficiently large number of processors is employed.
Gustafson’s law: for the special case that the sequential program part has a constant execution time, independently of the problem size.