Improving I/O performance through an in-kernel disk simulator

This paper presents two mechanisms that can significantly improve the I/O performance of both hard and solid-state drives for read operations: KDSim and REDCAP. KDSim is an in-kernel disk simulator that provides a framework for simultaneously simulating the performance obtained by different I/O system mechanisms and algorithms, and for dynamically turning them on and off, or selecting between different options or policies, to improve the overall system performance. REDCAP is a RAM-based disk cache that effectively enlarges the built-in cache present in disk drives. By using KDSim, this cache is dynamically activated/deactivated according to the throughput achieved. Results show that, by using KDSim and REDCAP together, a system can improve its I/O performance up to 88% for workloads with some spatial locality on both hard and solid-state drives, while it achieves the same performance as a ‘regular system’ for workloads with random or sequential access patterns.Peer ReviewedPostprint (author's final draft

Improving Parallel I/O Performance Using Interval I/O

Today\u27s most advanced scientific applications run on large clusters consisting of hundreds of thousands of processing cores, access state of the art parallel file systems that allow files to be distributed across hundreds of storage targets, and utilize advanced interconnections systems that allow for theoretical I/O bandwidth of hundreds of gigabytes per second. Despite these advanced technologies, these applications often fail to obtain a reasonable proportion of available I/O bandwidth. The reasons for the poor performance of application I/O include the noncontiguous I/O access patterns used for scientific computing, contention due to false sharing, and the somewhat finicky nature of parallel file system performance. We argue that a more fundamental cause of this problem is the legacy view of a file as a linear sequence of bytes. To address these issues, we introduce a novel approach for parallel I/O called Interval I/O. Interval I/O is an innovative approach that uses application access patterns to partition a file into a series of intervals, which are used as the fundamental unit for subsequent I/O operations. Use of this approach provides superior performance for the noncontiguous access patterns which are frequently used by scientific applications. In addition, the approach reduces false contention and the unnecessary serialization it causes. Interval I/O also significantly increases the performance of atomic mode operations. Finally, the Interval I/O approach includes a technique for supporting parallel I/O for cooperating applications. We provide a prototype implementation of our Interval I/O system and use it to demonstrate performance improvements of as much as 1000% compared to ROMIO when using Interval I/O with several common benchmarks

A Study of I/O Performance of Virtual Machines

In this study, we investigate some counterintuitive but frequent performance issues that arise when doing high-speed networking (or I/O in general) with Virtual Machines (VMs). VMs use one or more single-producer/single-consumer systems to exchange I/O data (e.g. network packets) with their hypervisor. We show that when the producer and the consumer process packets at different rates, the high cost required for synchronization (interrupts and ‘kicks’) may reduce throughput of the system well below the slowest of the two parties; moreover, accelerating the faster party may cause the throughput to decrease. Our work provides a model for throughput, efficiency and latency of producer/consumer systems when notifications or sleeping are used as a synchronization mechanism; identifies different operating regimes depending on the operating parameters; validates the accuracy of our model against a VirtIO-based prototype, taking into account most of the details of real-world deployments; provides practical and robust strategies to maximize throughput and minimize energy while keeping the latency under control, without depending on precise timing measurements nor unreasonable assumptions on the system’s behavior. The study is particularly interesting for Network Function Virtualization deployments, where high-rate producer/consumer systems in virtualized environments are the core components

Optimizing Virtual Machine I/O Performance in Cloud Environments

Maintaining closeness between data sources and data consumers is crucial for workload I/O performance. In cloud environments, this kind of closeness can be violated by system administrative events and storage architecture barriers. VM migration events are frequent in cloud environments. VM migration changes VM runtime inter-connection or cache contexts, significantly degrading VM I/O performance. Virtualization is the backbone of cloud platforms. I/O virtualization adds additional hops to workload data access path, prolonging I/O latencies. I/O virtualization overheads cap the throughput of high-speed storage devices and imposes high CPU utilizations and energy consumptions to cloud infrastructures. To maintain the closeness between data sources and workloads during VM migration, we propose Clique, an affinity-aware migration scheduling policy, to minimize the aggregate wide area communication traffic during storage migration in virtual cluster contexts. In host-side caching contexts, we propose Successor to recognize warm pages and prefetch them into caches of destination hosts before migration completion. To bypass the I/O virtualization barriers, we propose VIP, an adaptive I/O prefetching framework, which utilizes a virtual I/O front-end buffer for prefetching so as to avoid the on-demand involvement of I/O virtualization stacks and accelerate the I/O response. Analysis on the traffic trace of a virtual cluster containing 68 VMs demonstrates that Clique can reduce inter-cloud traffic by up to 40%. Tests of MPI Reduce_scatter benchmark show that Clique can keep VM performance during migration up to 75% of the non-migration scenario, which is more than 3 times of the Random VM choosing policy. In host-side caching environments, Successor performs better than existing cache warm-up solutions and achieves zero VM-perceived cache warm-up time with low resource costs. At system level, we conducted comprehensive quantitative analysis on I/O virtualization overheads. Our trace replay based simulation demonstrates the effectiveness of VIP for data prefetching with ignorable additional cache resource costs

Optimizing the FPGA memory design for a Sobel edge detector

This paper explores different memory systems by investigating the trade-offs involved with choosing one memory system over another on an FPGA. As an example, we use a Sobel edge detector to look at the trade-offs for different memory components. We demonstrate how each type of memory affects I/O performance and area. By exploiting these trade-offs in performance and area a designer should be able to find an optimum on-chip memory system for a given application

Predicting I/O performance in HPC using artificial neural networks

The prediction of file access times is an important part for the modeling of supercomputer's storage systems. These models can be used to develop analysis tools which support the users to integrate efficient I/O behavior. In this paper, we analyze and predict the access times of a Lustre file system from the client perspective. Therefore, we measure file access times in various test series and developed different models for predicting access times. The evaluation shows that in models utilizing artificial neural networks the average prediciton error is about 30% smaller than in linear models. A phenomenon in the distribution of file access times is of particular interest: File accesses with identical parameters show several typical access times.The typical access times usually differ by orders of magnitude and can be explained with a different processing of the file accesses in the storage system - an alternative I/O path. We investigate a method to automatically determine the alternative I/O path and quantify the significance of knowledge about the internal processing. It is shown that the prediction error is improved significantly with this approach

I/O performance evaluation with Parabench — programmable I/O benchmark

AbstractChoosing an appropriate cluster file system for a specific high performance computing application is challenging and depends mainly on the specific application I/O needs. There is a wide variety of I/O requirements: Some implementations require reading and writing large datasets, others out-of-core data access, or they have database access requirements. Application access patterns reflect different I/O behavior and can be used for performance testing.This paper presents the programmable I/O benchmarking tool Parabench. It has access patterns as input, which can be adapted to mimic behavior for a rich set of applications. Using this benchmarking tool, composed patterns can be automatically tested and easily compared on different local and cluster file systems. Here we introduce the design of the proposed benchmark, focusing on the Parabench programming language, which was developed for flexible pattern creation. We also demonstrate here an exemplary usage of Parabench and its capabilities to handle the POSIX and MPI-IO interfaces

A performance analysis of the PASLIB version 2.1X SEND and RECV routines on the finite element machine

The Finite Element Machine is an experimental array processor designed to support research in parallel algorithms and architectures. This report presents a case study of communications using the SENDa and RECV system software routines on the Finite Element Machine, followed by a discussion of the effect of I/O performance on the efficiency of parallel algorithms

I/O Performance Characterization of Lustre and NASA Applications on Pleiades

In this paper we study the performance of the Lustre file system using five scientific and engineering applications representative of NASA workload on large-scale supercomputing systems such as NASA s Pleiades. In order to facilitate the collection of Lustre performance metrics, we have developed a software tool that exports a wide variety of client and server-side metrics using SGI's Performance Co-Pilot (PCP), and generates a human readable report on key metrics at the end of a batch job. These performance metrics are (a) amount of data read and written, (b) number of files opened and closed, and (c) remote procedure call (RPC) size distribution (4 KB to 1024 KB, in powers of 2) for I/O operations. RPC size distribution measures the efficiency of the Lustre client and can pinpoint problems such as small write sizes, disk fragmentation, etc. These extracted statistics are useful in determining the I/O pattern of the application and can assist in identifying possible improvements for users applications. Information on the number of file operations enables a scientist to optimize the I/O performance of their applications. Amount of I/O data helps users choose the optimal stripe size and stripe count to enhance I/O performance. In this paper, we demonstrate the usefulness of this tool on Pleiades for five production quality NASA scientific and engineering applications. We compare the latency of read and write operations under Lustre to that with NFS by tracing system calls and signals. We also investigate the read and write policies and study the effect of page cache size on I/O operations. We examine the performance impact of Lustre stripe size and stripe count along with performance evaluation of file per process and single shared file accessed by all the processes for NASA workload using parameterized IOR benchmark