This paper presents a hardware/software co-design solution to efficiently implement block-layer deduplication over SSD arrays. By introducing complex and varying dependency over the entire storage space, deduplication is infamously subject to high storage management overheads in terms of CPU/memory resource usage and I/O performance degradation. To fundamentally address this problem, one intuitive idea is to offload deduplication storage management from host into SSDs, which is motivated by the redundant dual address mapping in host-side deduplication layer and intra-SSD flash translation layer (FTL). The practical implementation of this idea is nevertheless challenging because of the array-wide deduplication vs. per-SSD FTL management scope mismatch. Aiming to tackle this challenge, this paper presents a solution, called ARM-Dedup, that makes SSD FTL deduplication-oriented and array-aware and accordingly re-architects deduplication software to achieve lightweight and high-performance deduplication over an SSD array. We implemented an ARM-Dedup prototype based on the Linux Dmdedup engine and mdraid software RAID over FEMU SSD emulators. Experimental results show that ARM-Dedup has good scalability and can improve system performance significantly, such as by up to 272% and 127% higher IOPS in synthetic and real-world workloads, respectively.

Sharing a flash-based solid-state drive (SSD) among multiple tenants has become a common practice to improve storage utilization and cost efficiency. Meanwhile, how to allocate limited storage resources, especially the over-provisioning space (OPS) resources, among competitive tenants has emerged as a critical problem. The OPS refers to additional user-invisible storage space, whose size influences garbage collection (GC) efficiency. Due to unawareness of workload characteristics of different tenants, prior studies on multitenant OPS allocation lead to suboptimal SSD performance. In this article, we propose a novel workload-adaptive OPS allocation scheme for multitenant SSDs, called WA-OPShare. It targets an OPS sharing scheme that dynamically allocates the OPS among tenants to improve overall SSD performance. Two models are developed to identify underutilized storage space and predict the OPS-induced performance benefit of each tenant, respectively. Guided by the models, WA-OPShare regularly releases the underutilized storage space and then reallocates it to the tenant who can benefit the most. Experimental results show that compared to the traditional Partition and Sharing schemes, WA-OPShare improves the performance by up to 40.3% and 31.2%, and reduces the write amplification by up to 37.0% and 17.5%, respectively.

In order to provide Java with the ability for supporting scientific parallel computing, we introduce a data parallel extension to Java language with runtime system support. We provide the distributed array extension to Java, and discuss the related operation and control over the new distributed array. Communication involving distributed arrays are handles through a standard of a collective communication library. We consider the programming in a Single Program Multiple Data (SPMD) model.

The NPAC kernel runtime, developed in the PCRC (Parallel Compiler Runtime Consortium) project, is a runtime library with special support for the High Performance Fortran data model. It provides array descriptors for a generalized class of HPF like distributed arrays, support for parallel access to their elements, and a rich library of collective communication and arithmetic operations for manipulating these arrays. The library has been successfully used as a component in experimental HPF translation systems. With prospects for early appearance of fully featured, efficient HPF compilers looking questionable, we discuss a class of more easily implementable data parallel language extensions that preserve many of the attractive features of HPF, while providing the programmer with direct access to runtime libraries such as the NPAC PCRC kernel.

We outline an extension of Java for programming with distributed arrays. The basic programming style is Single Program Multiple Data (SPMD), but parallel arrays are provided as new language primitives. Further extensions include three distributed control constructs, the most important being a data-parallel loop construct. Communications involving distributed arrays are handled through a standard library of collective operations. Because the underlying programming model is SPMD programming, direct calls to MPI or other communication packages are also allowed in an HPJava program. © 1998 John Wiley & Sons, Ltd.