In the internet era, where people are connected to each other, web server architectures have been developed and advanced. Now, we are witnessing the advent of the complex IoT (Internet of Things) era, where not only people, but also all devices on the planet are interconnected to each other and enormous amount of interactions is required. In this paper, we propose HePA (Hexagonal Platform Architecture), a platform architecture that is extremely scalable while maintaining required performance and reflecting requirements of the complex environment. We expect the HePA to become a reference architecture in this field.

Although snoop-based coherence protocols provide fast cache-to-cache transfers with a simple and robust coherence mechanism, scaling the protocols has been difficult due to the overheads of broadcast snooping. In this paper, we propose a coherence filtering technique called subspace snooping, which stores the potential sharers of each memory page in the page table entry. By using the sharer information in the page table entry, coherence transactions for a page generate snoop requests only to the subset of nodes in the system. However, the coherence subspace of a page may evolve, as the phases of applications may change or the operating system may migrate threads to different nodes. To adjust subspaces dynamically, subspace snooping supports two different shrinking mechanisms, which remove obsolete nodes from subspaces. Among the two shrinking mechanisms, subspace snooping with safe shrinking can be integrated to any type of coherence protocols and network topologies, as it guarantees that a subspace always contains the precise sharers of a page. Speculative shrinking breaks the subspace superset property, but achieves better snoop reductions than safe shrinking. We evaluate subspace snooping with Token Coherence on unordered mesh networks. Subspace snooping reduces 58 percent of snoops on average for a set of parallel scientific and server workloads, and 87 percent for our multiprogrammed workloads.

Solid state disks (SSDs) have many advantages over hard disk drives, including better reliability, performance, durability, and power efficiency. However, the characteristics of SSDs are completely different from those of hard disk drives with rotating disks. To achieve the full potential performance improvement with SSDs, operating systems or applications must understand the critical performance parameters of SSDs to fine-tune their accesses. However, the internal hardware and software organizations vary significantly among SSDs and, thus, each SSD exhibits different parameters which influence the overall performance. In this paper, we propose a methodology which can extract several essential parameters affecting the performance of SSDs, and apply the extracted parameters to SSD systems for performance improvement. The target parameters of SSDs considered in this paper are 1) the size of read/write unit, 2) the size of erase unit, 3) the size of read buffer, and 4) the size of write buffer. We modify two operating system components to optimize their operations with the SSD parameters. The experimental results show that such parameter-aware management leads to significant performance improvements for large file accesses by performing SSD-specific optimizations.

Although snoop-based coherence protocols provide fast cache-to-cache transfers with a simple and robust coherence mechanism, scaling the protocols has been difficult due to the overheads of broadcast snooping. In this paper, we propose a coherence filtering technique called subspace snooping, which stores the potential sharers of each memory page in the page table entry. By using the sharer information in the page table entry, coherence transactions for a page generate snoop requests only to the subset of nodes in the system (subspace). However, the coherence subspace of a page may evolve, as the phases of applications may change or the operating system may migrate threads to different nodes. To adjust subspaces dynamically, subspace snooping supports a shrinking mechanism, which removes obsolete nodes from subspaces.

Various scientific computations have become so complex, and thus computation tools play an important role. In this paper, we explore the state-of-the-art framework providing high-level matrix computation primitives with MapReduce through the case study approach, and demonstrate these primitives with different computation engines to show the performance and scalability. We believe the opportunity for using MapReduce in scientific computation is even more promising than the success to date in the parallel systems literature.

Solid state disks (SSDs) consisting of NAND flash memory are being widely used in laptops, desktops, and even enterprise servers. SSDs have many advantages over hard disk drives (HDDs) in terms of reliability, performance, durability, and power efficiency. Typically, the internal hardware and software organization varies significantly from SSD to SSD and thus each SSD exhibits different parameters which influence the overall performance. In this paper, we propose a methodology which can extract several essential parameters affecting the performance of SSDs. The target parameters of SSDs considered in this paper are (1) the size of read/write unit, (2) the size of erase unit, (3) the type of NAND flash memory used, (4) the size of read buffer, and (5) the size of write buffer. Obtaining these parameters will allow us to understand the internal architecture of the target SSD better and to get the most performance out of SSD by performing SSD-specific optimizations.