The authors present methods to reduce computer energy consumption by means of a better usage of a specific set of resources and maximizing the efficiency of the running applications. The processor frequency is adjusted to the needs of the running job, leading to a power drop by a factor of 2 and doubling battery life time of laptops. It is shown how computer resources can be optimally adapted to application needs, reducing job run time. Examples on how to optimize algorithms on single node and parallel RISC architectures are discussed. The job-related data are stored and reused to help computer managers to replace machines.