The authors applied operations management principles on scheduling and allocation to scientific computing clusters to decrease energy consumption and to increase throughput. They challenged the traditional one job per one processor core scheduling method commonly used in scientific computing with parallel processing and bottleneck management. The authors tested the effect of increased parallelism by using different test applications related to high-energy physics computing. The test results showed that at best their methods both decreased energy consumption down to 40% and increased throughput up to 100%, compared to the standard one task per CPU core method. The trade-off is that processing times of individual tasks get longer, but in scientific computing, the overall throughput and energy-efficiency are often more important.