Graph pattern mining (GPM) is a class of algorithms widely used in many real-world applications in bio-medicine, e-commerce, security, social sciences, etc. GPM is a computationally intensive problem with an enormous amount of coarse-grain parallelism and therefore, attractive for hardware acceleration. Unfortunately, existing GPM accelerators have not used the best known algorithms and optimizations, and thus offer questionable benefits over software implementations.We present FlexMiner, a software/hardware co-designed GPM accelerator that improves the efficiency without compromising the generality or productivity of state-of-the-art software GPM frameworks. FlexMiner exploits massive amount of coarse-grain parallelism in GPM by deploying a large number of specialized processing elements. For efficient searches, the FlexMiner hardware accepts pattern-specific execution plans, which are generated automatically by the FlexMiner compiler from the given pattern(s). To avoid repetitive computation on neighborhood connectivity, we provide dedicated on-chip storage to memoize reusable connectivity information in a connectivity map (c-map ) which is implemented with low-cost yet high-throughput hardware. The on-chip memories in FlexMiner are managed dynamically using heuristics derived by the compiler, and thus are fully utilized. We have evaluated FlexMiner with 4 GPM applications on a wide range of real-world graphs. Our cycle-accurate simulation shows that FlexMiner with 64 PEs achieves 10.6× speedup on average over the state-of-the-art software system executing 20 threads on a 10-core Intel CPU.

We propose a new I/O architecture for NAND flash-based SSDs, called application-managed flash (AMF) and present two case studies to show its usefulness. In a typical SSD controller, an intermediate software layer, called the flash translation layer (FTL), is employed between NAND flash chips and a host interface. The main responsibility of an FTL is to provide interoperability with conventional HDDs, but this interoperability comes at the cost of extra hardware resources and degraded I/O performance. The proposed AMF refactors the flash storage architecture so that an SSD controller exposes append-only segments, which do not permit overwriting. This refactoring dramatically improves performance of applications and reduces hardware costs by allowing applications to directly manage flash storage with minimal supports from the SSD controller. In order to understand the benefits of AMF, we study two popular applications: a log-structured file system (F2FS) and a key-value store (RocksDB). Our experiments show that the DRAM in the flash controller is reduced by 128X and the performances of the file system and the key-value store improve by 80 and 54 percent, respectively, over conventional SSDs.

Key-value store based on a log-structured merge-tree (LSM-tree) is preferable to hash-based key-value store, because an LSM-tree can support a wider variety of operations and show better performance, especially for writes. However, LSM-tree is difficult to implement in the resource constrained environment of a key-value SSD (KV-SSD), and, consequently, KV-SSDs typically use hash-based schemes. We present PinK , a design and implementation of an LSM-tree-based KV-SSD, which compared to a hash-based KV-SSD, reduces 99th percentile tail latency by 73%, improves average read latency by 42%, and shows 37% higher throughput. The key idea in improving the performance of an LSM-tree in a resource constrained environment is to avoid the use of Bloom filters and instead, use a small amount of DRAM to keep/pin the top levels of the LSM-tree. We also find that PinK is able to provide a flexible design space for a wide range of KV workloads by leveraging the read-write tradeoff in LSM-trees.

We propose LightStore, a key-value flash store, as a substitute for x86-based storage servers. A LightStore node has a low-power embedded-class processor, a few gigabytes of DRAM and a few terabytes of NAND flash, and can be directly connected to a network port in a datacenter. A large-scale distributed storage cluster can be formed simply by adding more LightStore nodes to the network. Applications in a datacenter can take multiple software-defined views of LightStore stores via thin LightStore adapter layers, which translate conventional KV, YCSB, block, and file accesses to KV ones for LightStore. LightStore is estimated to be 2.0x power-efficient and 2.3x space-efficient than an x86-based all-flash array system of the same capacity. Experimental results on our LightStore prototype show that 1) the LightStore node performance is comparable to an x86 server with a single SSD; 2) a four-node LightStore cluster exhibits up to 7.4x better ops/J than an x86 server with four SSDs.