Private cache software software#
Private cache software code#

We then present a modified lmbench micro-benchmark suite to measure the cost of threading on these different hardware configurations. The simulated configuration represents shared cache CMP, private cache CMP and symmetrical multiprocessor (SMP) environment. In this paper, we present a novel approach for fair comparison of the hardware configuration by simulation through configuring a pair of quad-core processors. Furthermore, this problem multiplies itself with different hardware available.

Private cache software code#

Once they have identified the areas of parallelization, they will need to know the level of code granularity needed to ensure profitable execution.

Private cache software software#

N2 - With the wide availability of chip multi-processing (CMP), software developers are now facing the task of effectively parallelizing their software code. T1 - Pthreads performance characteristics on shared cache CMP, private cache CMP and SMP The techniques presented can be incorporated into integrated development environment, compilers and potentially even other run-time environments.', Private cache CMP and SMP do not exhibit significant difference in our measurements. However, the measurements also indicate that thread size is an important consideration where potential cache trashing can occur when sharing a cache between processing cores. In our empirical studies, we observe that shared cache CMP exhibits better performance when the operating systems load balancer is highly active. The techniques presented can be incorporated into integrated development environment, compilers and potentially even other run-time environments.Ībstract = 'With the wide availability of chip multi-processing (CMP), software developers are now facing the task of effectively parallelizing their software code.

With the wide availability of chip multi-processing (CMP), software developers are now facing the task of effectively parallelizing their software code.