This work deals with scheduling and checkpointing strategies to execute scientific workflows on failure-prone large-scale platforms. To the best of our knowledge, this work is the first to target fail-stop errors for arbitrary workflows. Most previous work addresses soft errors, which corrupt the task being executed by a processor but do not cause the entire memory of that processor to be lost, contrarily to fail-stop errors. We revisit classical mapping heuristics such as HEFT and MINMIN and complement them with several checkpointing strategies. The objective is to derive an efficient trade-off between checkpointing every task (CKPTALL), which is an overkill when failures are rare events, and checkpointing no task (CKPTNONE), which induces dramatic re-execution overhead even when only a few failures strike during execution. Contrarily to previous work, our approach applies to arbitrary workflows, not just special classes of dependence graphs such as M-SPGS (Minimal Series-Parallel Graphs). Extensive experiments report significant gain over both CKPTALL and CKPTNONE, for a wide variety of workflows.