Energy-Efficient Streaming Using Non-volatile Memory

The disk and the DRAM in a typical mobile system consume a significant fraction (up to 30%) of the total system energy. To save on storage energy, the DRAM should be small and the disk should be spun down for long periods of time. We show that this can be achieved for predominantly streaming workloads by connecting the disk to the DRAM via a large non-volatile memory (NVM). We refer to this as the NVM-based architecture (NVMBA); the conventional architecture with only a DRAM and a disk is referred to as DRAMBA. The NVM in the NVMBA acts as a traffic reshaper from the disk to the DRAM. The total system costs are balanced, since the cost increase due to adding the NVM is compensated by the decrease in DRAM cost. We analyze the energy saving of NVMBA, with NAND flash memory serving as NVM, relative to DRAMBA with respect to (1) the streaming demand, (2) the disk form factor, (3) the best-effort provision, and (4) the stream location on the disk. We present a worst-case analysis of the reliability of the disk drive and the flash memory, and show that a small flash capacity is sufficient to operate the system over a year at negligible cost. Disk lifetime is superior to flash, so that is of no concern

GraphR: Accelerating Graph Processing Using ReRAM

This paper presents GRAPHR, the first ReRAM-based graph processing accelerator. GRAPHR follows the principle of near-data processing and explores the opportunity of performing massive parallel analog operations with low hardware and energy cost. The analog computation is suit- able for graph processing because: 1) The algorithms are iterative and could inherently tolerate the imprecision; 2) Both probability calculation (e.g., PageRank and Collaborative Filtering) and typical graph algorithms involving integers (e.g., BFS/SSSP) are resilient to errors. The key insight of GRAPHR is that if a vertex program of a graph algorithm can be expressed in sparse matrix vector multiplication (SpMV), it can be efficiently performed by ReRAM crossbar. We show that this assumption is generally true for a large set of graph algorithms. GRAPHR is a novel accelerator architecture consisting of two components: memory ReRAM and graph engine (GE). The core graph computations are performed in sparse matrix format in GEs (ReRAM crossbars). The vector/matrix-based graph computation is not new, but ReRAM offers the unique opportunity to realize the massive parallelism with unprecedented energy efficiency and low hardware cost. With small subgraphs processed by GEs, the gain of performing parallel operations overshadows the wastes due to sparsity. The experiment results show that GRAPHR achieves a 16.01x (up to 132.67x) speedup and a 33.82x energy saving on geometric mean compared to a CPU baseline system. Com- pared to GPU, GRAPHR achieves 1.69x to 2.19x speedup and consumes 4.77x to 8.91x less energy. GRAPHR gains a speedup of 1.16x to 4.12x, and is 3.67x to 10.96x more energy efficiency compared to PIM-based architecture.Comment: Accepted to HPCA 201

Feasibility of Harvesting Power To Run A Domestic Water Meter Using Streaming Cell Technology

We investigate the possibility of using streaming cells as a means of harvesting energy from the town water supply. We measure the electrical power developed from streaming cells using tap water as a working fluid. We estimate the amount of energy available from a typical domestic household based on water usage data. We estimate the amount of energy required to operate a simple data logger and transmitter. From these estimates we calculate the required efficiency and physical form of a streaming cell energy converter. We comment on the feasibility of using streaming cell technology as a means of harvesting energy from a domestic water supply

Algorithm-Directed Crash Consistence in Non-Volatile Memory for HPC

Fault tolerance is one of the major design goals for HPC. The emergence of non-volatile memories (NVM) provides a solution to build fault tolerant HPC. Data in NVM-based main memory are not lost when the system crashes because of the non-volatility nature of NVM. However, because of volatile caches, data must be logged and explicitly flushed from caches into NVM to ensure consistence and correctness before crashes, which can cause large runtime overhead. In this paper, we introduce an algorithm-based method to establish crash consistence in NVM for HPC applications. We slightly extend application data structures or sparsely flush cache blocks, which introduce ignorable runtime overhead. Such extension or cache flushing allows us to use algorithm knowledge to \textit{reason} data consistence or correct inconsistent data when the application crashes. We demonstrate the effectiveness of our method for three algorithms, including an iterative solver, dense matrix multiplication, and Monte-Carlo simulation. Based on comprehensive performance evaluation on a variety of test environments, we demonstrate that our approach has very small runtime overhead (at most 8.2\% and less than 3\% in most cases), much smaller than that of traditional checkpoint, while having the same or less recomputation cost.Comment: 12 page

How migrating 0.0001% of address space saves 12% of energy in hybrid storage

We present a simple, operating-\ud system independent method to reduce the num-\ud ber of seek operations and consequently reduce\ud the energy consumption of a hybrid storage\ud device consisting of a hard disk and a flash\ud memory. Trace-driven simulations show that\ud migrating a tiny amount of the address space\ud (0.0001%) from disk to flash already results\ud in a significant storage energy reduction (12%)\ud at virtually no extra cost. We show that the\ud amount of energy saving depends on which part\ud of the address space is migrated, and we present\ud two indicators for this, namely sequentiality and\ud request frequency. Our simulations show that\ud both are suitable as criterion for energy-saving\ud file placement methods in hybrid storage. We\ud address potential wear problems in the flash\ud subsystem by presenting a simple way to pro-\ud long its expected lifetime.\u