Solid State Disk (SSD) arrays are in a position to (as least partially) replace spinning disk arrays in high performance computing (HPC) systems due to their better performance and lower power consumption. However, these emerging SSD arrays are facing enormous challenges, which are not observed in disk-based arrays. Specif cally, we observe that the performance of SSD arrays can signif cantly degrade due to various array-level resource contentions. In addition, their maintenance costs exponentially increase over time, which renders them diff cult to deploy widely in HPC systems. To address these challenges, we propose Triple-A, a non-SSD based Autonomic All-Flash Array, which is a self-optimizing, from-scratch NAND f ash cluster. Triple-A can detect two different types of resource contentions and autonomically alleviate them by reshaping the physical data-layout on its f ash array network. Our experimental evaluation using both real workloads and a micro-benchmark show that Triple-A can offer a 53% higher sustained throughput and a 80% lower I/O latency than non-autonomic SSD arrays.