Summary Energy‐efficient scheduling is a step towards meeting green computing requirements. The work in this direction mainly aims at reducing dynamic energy consumption that includes clock gating, cache subbanking, and dynamic voltage and frequency scaling of underlying processors. However, the emergence of fast and compact transistor sizes has exponentially added onto the processor static power consumption, which has not been paid much attention. This article proposes a duplication‐controlled static energy‐efficient scheduling (C‐SEED) algorithm for scheduling precedence constrained applications on parallel computing systems. The C‐SEED algorithm couples adaptive threshold‐based duplication with system level dynamic power management technique to achieve its objectives. Dynamic power management works by selectively putting the energy‐consuming resources to efficient low‐power states for idle times to reduce energy consumption. Efficacy of the proposed algorithm is analyzed and compared against other relevant works on the basis of makespan and total energy (dynamic + static + communication) consumption. The extensive simulation results carried over large set of random and regular task graphs show that the proposed C‐SEED algorithm has potential to reduce energy consumption as well as makespan.

Scheduling precedence constrained task graphs, with or without duplication, is one of the most challenging NP-complete problems in parallel and distributed computing systems. Duplication heuristics are more effective, in general, for fine grain task graphs and for networks with high communication latencies. However, most of the available duplication algorithms are designed under the assumption of unbounded availability of fully connected processors, and lie in a high complexity range. Low complexity optimal duplication algorithms work under restricted cost and/or shape parameters for the task graphs. Further, the required number of processors grows in proportion to the task-graph size significantly. An improved duplication strategy is proposed that works for arbitrary task graphs, with a limited number of interconnection-constrained processors. Unlike most other algorithms that replicate all possible parents/ancestors of a given task, the proposed algorithm tends to avoid redundant duplications and duplicates the nodes selectively, only if it helps in improving the performance. This results in lower duplications and also lower time and space complexity. Simulation results are presented for clique and an interconnection-constrained network topology with random and regular benchmark task graph suites, representing a variety of parallel numerical applications. Performance, in terms of normalized schedule length and efficiency, is compared with some of the well-known and recently proposed algorithms. The suggested algorithm turns out to be most efficient, as it generates better or comparable schedules with remarkably less processor consumption.