Software Persistent Memory

Total Page:16

File Type:pdf, Size:1020Kb

Software Persistent Memory Software Persistent Memory Jorge Guerra, Leonardo M´armol, Daniel Campello, Carlos Crespo, Raju Rangaswami, Jinpeng Wei Florida International University {jguerra, lmarm001, dcamp020, ccres008, raju, weijp}@cs.fiu.edu Abstract translators increase overhead along the data path. Most Persistence of in-memory data is necessary for many importantly, all of these solutions require substantial ap- classes of application and systems software. We pro- plication involvement for making data persistent which pose Software Persistent Memory (SoftPM), a new mem- ultimately increases code complexity affecting reliabil- ory abstraction which allows malloc style allocations ity, portability, and maintainability. to be selectively made persistent with relative ease. In this paper, we present Software Persistent Mem- Particularly, SoftPM’s persistent containers implement ory (SoftPM), a lightweight persistent memory abstrac- automatic, orthogonal persistence for all in-memory tion for C. SoftPM provides a novel form of orthogo- data reachable from a developer-defined root structure. nal persistence [8], whereby the persistence of data (the Writing new applications or adapting existing applica- how) is seamless to the developer, while allowing effort- tions to use SoftPM only requires identifying such root less control over when and what data persists. To use structures within the code. We evaluated the correct- SoftPM, developers create one or more persistent con- ness, ease of use, and performance of SoftPM using tainers to house a subset of in-memory data that they a suite of microbenchmarks and real world applica- wish to make persistent. They only need to ensure that tions including a distributed MPI application, SQLite (an a container’s root structure houses pointers to the data in-memory database), and memcachedb (a distributed structures they wish to make persistent (e.g. the head of memory cache). In all cases, SoftPM was incorporated a list or the root of a tree). SoftPM automatically dis- with minimal developer effort, was able to store and re- covers data reachable from a container’s root structure cover data successfully, and provide significant perfor- (by recursively following pointers) and makes all new mance speedup (e.g., up to 10X for memcachedb and and modified data persistent. Restoring a container re- 83% for SQLite). turns the container root structure from which all origi- nally reachable data can be accessed. SoftPM thus ob- 1 Introduction viates the need for explicitly managing persistent data Persistence of in-memory data is necessary for many and places no restrictions on persistent data locations in classes of software including metadata persistence in the process’ address space. Finally, SoftPM improves systems software [21, 24, 32, 33, 35, 38, 40, 47, 48, 52, I/O performance by eliminating the need to serialize data 59], application data persistence in in-memory databases and by using a novel chunk-remapping technique which and key-value stores [3, 5], and computational state per- utilizes the property that all container data is memory sistence in high-performance computing (HPC) applica- resident and trades writing additional data for reducing tions [19, 44]. Currently such software relies on the overall I/O latency. persistence primitives provided by the operating system We evaluated a Linux prototypeof SoftPM for correct- (file or block I/O) or a database system. When using ness, ease of use, and performance using microbench- OS primitives, developers need to carefully track persis- marks and three real world applications including a re- tent data structures in their code and ensure the atomic- coverable distributed MPI application, SQLite [5] (a ity of persistent modifications. Additionally they are re- serverless database), and memcachedb (a distributed quired to implement serialization/deserialization for their memory cache). In all cases, we could integrate SoftPM structures, potentially creating and managing additional with little developer effort and store and recover appli- metadata whose modifications must also be made con- cation data successfully. In comparison to explicitly sistent with the data they represent. On the other hand, managing persistence within code, development com- using databases for persistent metadata is generally not plexity substantially reduced with SoftPM. Performance an option within systems software, and when their use is improvements were up to 10X for memcachedb and possible, developers must deal with data impedance mis- 83% for SQLite, when compared to their native, opti- match [34]. While some development complexity is al- mized, persistence implementations. Finally, for a HPC- leviated by object-relation mapping libraries [10], these class matrix multiplication application, SoftPM’s asyn- 1 Function Description int pCAlloc(int magic, int cSSize, void ∗∗ cStruct) create a new container; returns a container identifier int pCSetAttr(int cID, struct cattr ∗ attr) set container attributes; reports success or failure struct cattr ∗ pCGetAttr(int magic) get attributes of an existing container; returns container attributes void pPoint(int cID) create a persistence point asynchronously int pSync(int cID) sync-commit outstanding persistence point I/Os; reports success or failure int pCRestore(int magic, void ∗∗ cStruct) restore a container; populates container struct, returns a container identifier void pCFree(int cID) free all in-memory container data void pCDelete(int magic) delete on-disk and in-memory container data void pExclude(int cID, void ∗ ptr) do not follow pointer during container discovery Table 1: The SoftPM application programmer interface. Network LIMA SID PFS module App1 Container Discovery & Chunk SSD module A Manager Allocator Remapper App2 P Cache module memcachedb I Write Transaction Flusher Appn Handler Handler HDD module Figure 1: The SoftPM architecture. Container Root Usage and the Storage-optimized I/O Driver (SID) as depicted Structure in Figure 1. LIMA’s container manager handles con- struct c root id = pCAlloc(m,sizeof(*cr),&cr); { tainer creation. LIMA manages the container’s persis- list t *l; cr->l = list head; tent data as a collection of memory pages marked for } *cr; pPoint(id); persistence. When creating a persistence point, the dis- covery and allocator module moves any data newly made Figure 2: Implementing a persistent list. pCAlloc reachable from the container root structure and located in allocates a container and pPoint makes it persistent. volatile memory to these pages. Updates to these pages are tracked by the write handler at the granularity of chronous persistence feature provided performance at multi-page chunks. When requested to do so, the flusher close to memory speeds without compromising data con- creates persistence points and sends the dirty chunks to sistency and recoverability. the SID layer in an asynchronous manner. Restore re- quests are translated into chunks requests for SID. 2 SoftPM Overview The SID layer atomically commits container data to SoftPM implements a persistent memory abstraction persistent storage and tunes I/O operations to the under- called container. To use this abstraction, applications lying storage mechanism. LIMA’s flusher first notifies create one or more containers and associate a root struc- the transaction handler of a new persistence point and ture with each. When the application requests a persis- submits dirty chunks to SID. The chunk remapper im- tence point, SoftPM calculates a memory closure that plements a novel I/O technique which uses the property contains all data reachable (recursively via pointers) that all container data is memory resident and trades writ- from the container root, and writes it to storage atomi- ing additional data for reducing overall I/O latency. We cally and (optionally) asynchronously. designed and evaluated SID implementations for hard The container root structure serves two purposes: (i) drive, SSD, and memcached back-ends. it frees developers from the burden of explicitly track- ing persistent memory areas, and (ii) it provides a simple 3 LIMA Design mechanism for accessing all persistent memory data af- ter a restore operation. Table 1 summarizes the SoftPM Persistent containers build a foundation to provide seam- API. In the simplest case, an application would create less memory persistence. Container data is managed one container and create persistence points as necessary within a contiguous container virtual address space, a (Figure 2). Upon recovery, a pointer to a valid container self-describing unit capable of being migrated across root structure is returned. systems and applications running on the same hardware architecture. The container virtual address space is com- 2.1 System Architecture posed solely of pages marked for persistence including The SoftPM API is implemented by two components: those containing application data and others used to store the Location Independent Memory Allocator (LIMA), LIMA metadata. This virtual address space is mapped to 2 Process Virtual Address Space LIMA Virtual Volume SID Physical Volume automatically detecting pointers in process memory, re- Container Chunk Page Table Ind. Map cursively moving data reachable from the container root <page> <chunk> to the container data pages, and fixing any back refer- . ences (other pointers) to the data that was moved. In . our implementation, this process is triggered each time <container> . a persistence point is requested by the application and is executed atomically by blocking all
Recommended publications
  • Efficient Support of Position Independence on Non-Volatile Memory
    Efficient Support of Position Independence on Non-Volatile Memory Guoyang Chen Lei Zhang Richa Budhiraja Qualcomm Inc. North Carolina State University Qualcomm Inc. [email protected] Raleigh, NC [email protected] [email protected] Xipeng Shen Youfeng Wu North Carolina State University Intel Corp. Raleigh, NC [email protected] [email protected] ABSTRACT In Proceedings of MICRO-50, Cambridge, MA, USA, October 14–18, 2017, This paper explores solutions for enabling efficient supports of 13 pages. https://doi.org/10.1145/3123939.3124543 position independence of pointer-based data structures on byte- addressable None-Volatile Memory (NVM). When a dynamic data structure (e.g., a linked list) gets loaded from persistent storage into 1 INTRODUCTION main memory in different executions, the locations of the elements Byte-Addressable Non-Volatile Memory (NVM) is a new class contained in the data structure could differ in the address spaces of memory technologies, including phase-change memory (PCM), from one run to another. As a result, some special support must memristors, STT-MRAM, resistive RAM (ReRAM), and even flash- be provided to ensure that the pointers contained in the data struc- backed DRAM (NV-DIMMs). Unlike on DRAM, data on NVM tures always point to the correct locations, which is called position are durable, despite software crashes or power loss. Compared to independence. existing flash storage, some of these NVM technologies promise This paper shows the insufficiency of traditional methods in sup- 10-100x better performance, and can be accessed via memory in- porting position independence on NVM. It proposes a concept called structions rather than I/O operations.
    [Show full text]
  • English Arabic Technical Computing Dictionary
    English Arabic Technical Computing Dictionary Arabeyes Arabisation Team http://wiki.arabeyes.org/Technical Dictionary Versin: 0.1.29-04-2007 April 29, 2007 This is a compilation of the Technical Computing Dictionary that is under development at Arabeyes, the Arabic UNIX project. The technical dictionary aims to to translate and standardise technical terms that are used in software. It is an effort to unify the terms used across all Open Source projects and to present the user with consistant and understandable interfaces. This work is licensed under the FreeBSD Documentation License, the text of which is available at the back of this document. Contributors are welcome, please consult the URL above or contact [email protected]. Q Ì ÉJ ªË@ éÒ¢@ Ñ«YË QK AK.Q« ¨ðQåÓ .« èQK ñ¢ ÕæK ø YË@ úæ®JË@ úGñAm '@ ñÓA®ÊË éj èYë . l×. @QK. éÔg. QK ú¯ éÊÒªJÖÏ@ éJ J®JË@ HAjÊ¢Ö Ï@ YJ kñKð éÔg. QK úÍ@ ñÓA®Ë@ ¬YîE .ºKñJ ËAK. éîD J.Ë@ ÐYjJÒÊË éÒj. Óð éÓñê®Ó H. ñAg éêk. @ð Õç'Y®JË ð á ÔgQÖÏ@ á K. H. PAJË@ øXA®JË ,H. ñAmÌ'@ . ¾JÖ Ï .ñÓA®Ë@ éK AîE ú ¯ èQ¯ñJÖÏ@ ð ZAKñÊË ø X @ ú G. ø Q¯ ékP ù ë ñÓA®Ë@ ékP . éJ K.QªËAK. ÕÎ @ [email protected] . úΫ ÈAB@ ð@ èC«@ à@ñJªË@ úÍ@ H. AëYË@ ZAg. QË@ ,á ÒëAÖÏ@ ɾK. I. kQK A Abortive release êm .× (ú¾J.) ¨A¢®K@ Abort Aêk . @ Abscissa ú æJ Absolute address Ê¢Ó à@ñ J« Absolute pathname Ê¢Ó PAÓ Õæ @ Absolute path Ê¢Ó PAÓ Absolute Ê¢Ó Abstract class XQm.× ­J Abstract data type XQm.× HA KAJ K.
    [Show full text]
  • Database : a Toolkit for Constructing Memory Mapped Databases Peter A
    Database : A Toolkit for Constructing Memory Mapped Databases Peter A. Buhr, Anil K. Goel and Anderson Wai Dept. of Computer Science, University of Waterloo Waterloo, Ontario, Canada, N2L 3G1 Abstract The main objective of this work was an ef®cient methodology for constructing low-level database tools that are built around a single-level store implemented using memory mapping. The methodology allowed normal programming pointers to be stored directly onto secondary storage, and subsequently retrieved and manipulated by other programs without the need for relocation, pointer swizzling or reading all the data. File structures for a database, e.g. a B- Tree, built using this approach are signi®cantly simpler to build, test, and maintain than traditional ®le structures. All access methods to the ®le structure are statically type-safe and ®le structure de®nitions can be generic in the type of the record and possibly key(s) stored in the ®le structure, which affords signi®cant code reuse. An additional design requirement is that multiple ®le structures may be simultaneously accessible by an application. Concurrency at both the front end (multiple accessors) and the back end (®le structure partitioned over multiple disks) are possible. Finally, experimental results between traditional and memory mapped ®les structures show that performance of a memory mapped ®le structure is as good or better than the traditional approach. 1 Introduction The main objective of this work was an ef®cient methodology for constructing low-level database tools that are built around a single-level store. A single-level store gives the illusion that data on disk (secondary storage) is accessible in the same way as data in main memory (primary storage), which is analogous to the goals of virtual memory.
    [Show full text]
  • Exact Positioning of Data Approach to Memory Mapped Persistent Stores: Design, Analysis and Modelling
    Exact Positioning of Data Approach to Memory Mapped Persistent Stores: Design, Analysis and Modelling by Anil K. Goel A thesis presented to the University of Waterloo in fulfilment of the thesis requirement for the degree of Doctor of Philosophy in Computer Science Waterloo, Ontario, Canada, 1996 c Anil K. Goel 1996 I hereby declare that I am the sole author of this thesis. I authorize the University of Waterloo to lend this thesis to other institutions or indi- viduals for the purpose of scholarly research. I further authorize the University of Waterloo to reproduce this thesis by photocopy- ing or by other means, in total or in part, at the request of other institutions or individuals for the purpose of scholarly research. iii The University of Waterloo requires the signatures of all persons using or photocopy- ing this thesis. Please sign below, and give address and date. v Abstract One of the primary functions of computers is to store information, i.e., to deal with long lived or persistent data. Programmers working with persistent data structures are faced with the problem that there are two, mostly incompatible, views of structured data, namely data in primary and secondary storage. Traditionally, these two views of data have been dealt with independently by researchers in the programming language and database communities. Significant research has occurred over the last decade on efficient and easy-to-use methods for manipulating persistent data structures in a fashion that makes the sec- ondary storage transparent to the programmer. Merging primary and secondary storage in this manner produces a single-level store, which gives the illusion that data on sec- ondary storage is accessible in the same way as data in primary storage.
    [Show full text]
  • Leanstore: In-Memory Data Management Beyond Main Memory
    LeanStore: In-Memory Data Management Beyond Main Memory Viktor Leis, Michael Haubenschild∗, Alfons Kemper, Thomas Neumann Technische Universitat¨ Munchen¨ Tableau Software∗ fleis,kemper,[email protected] [email protected]∗ Abstract—Disk-based database systems use buffer managers 60K in order to transparently manage data sets larger than main 40K memory. This traditional approach is effective at minimizing the number of I/O operations, but is also the major source of 20K overhead in comparison with in-memory systems. To avoid this TPC-C [txns/s] 0 overhead, in-memory database systems therefore abandon buffer BerkeleyDB LeanStore in-memoryWiredTiger management altogether, which makes handling data sets larger Fig. 1. Single-threaded in-memory TPC-C performance (100 warehouses). than main memory very difficult. In this work, we revisit this fundamental dichotomy and design Two representative proposals for efficiently managing larger- a novel storage manager that is optimized for modern hardware. Our evaluation, which is based on TPC-C and micro benchmarks, than-RAM data sets in main-memory systems are Anti- shows that our approach has little overhead in comparison Caching [7] and Siberia [8], [9], [10]. In comparison with a with a pure in-memory system when all data resides in main traditional buffer manager, these approaches exhibit one major memory. At the same time, like a traditional buffer manager, weakness: They are not capable of maintaining a replacement it is fully transparent and can manage very large data sets strategy over relational and index data. Either the indexes, effectively. Furthermore, due to low-overhead synchronization, our implementation is also highly scalable on multi-core CPUs.
    [Show full text]
  • A Survey of B-Tree Logging and Recovery Techniques
    1 A Survey of B-Tree Logging and Recovery Techniques GOETZ GRAEFE, Hewlett-Packard Laboratories B-trees have been ubiquitous in database management systems for several decades, and they serve in many other storage systems as well. Their basic structure and their basic operations are well understood including search, insertion, and deletion. However, implementation of transactional guarantees such as all-or-nothing failure atomicity and durability in spite of media and system failures seems to be difficult. High-performance techniques such as pseudo-deleted records, allocation-only logging, and transaction processing during crash recovery are widely used in commercial B-tree implementations but not widely understood. This survey collects many of these techniques as a reference for students, researchers, system architects, and software developers. Central in this discussion are physical data independence, separation of logical database contents and physical representation, and the concepts of user transactions and system transactions. Many of the techniques discussed are applicable beyond B-trees. Categories and Subject Descriptors: H.2.2 [Database Management]: Physical Design—Recovery and restart; H.2.4 [Database Management]: Systems—Transaction Processing; H.3.0 [Information Storage and Retrieval]: General General Terms: Algorithms ACM Reference Format: Graefe, G. 2012. A survey of B-tree logging and recovery techniques. ACM Trans. Datab. Syst. 37, 1, Article 1 (February 2012), 35 pages. DOI = 10.1145/2109196.2109197 http://doi.acm.org/10.1145/2109196.2109197 1. INTRODUCTION B-trees [Bayer and McCreight 1972] are primary data structures not only in databases but also in information retrieval, file systems, key-value stores, and many application- specific indexes.
    [Show full text]
  • R-Code a Very Capable Virtual Computer
    R-Code a Very Capable Virtual Computer The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Walton, Robert Lee. 1995. R-Code a Very Capable Virtual Computer. Harvard Computer Science Group Technical Report TR-37-95. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:26506458 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA RCo de a Very Capable Virtual Computer Rob ert Lee Walton TR Octob er Center for Research in Computing Technology Harvard University Cambridge Massachusetts RCODE A Very Capable Virtual Computer A thesis presented by Rob ert Lee Walton to The Division of Applied Sciences in partial fulllment of the requirements for the degree of Do ctor of Philosophy in the sub ject of Computer Science Harvard University Cambridge Massachusetts Octob er c by Rob ert Lee Walton All rights reserved iii ABSTRACT This thesis investigates the design of a machine indep endent virtual computer the R CODE computer for use as a target by high level language compilers Unlike previous machine indep endent targets RCODE provides higher level capabilities such as a garbage collecting memory manager tagged data type maps array descriptors register dataow semantics and a shared ob ject memory Emphasis is on trying to nd universal versions of these
    [Show full text]
  • Lazy Reference Counting for Transactional Storage Systems
    Lazy Reference Counting for Transactional Storage Systems Miguel Castro, Atul Adya, Barbara Liskov Lab oratory for Computer Science, Massachusetts Institute of Technology, 545 Technology Square, Cambridge, MA 02139 fcastro,adya,[email protected] Abstract ing indirection table entries in an indirect pointer swizzling scheme. Hac isanovel technique for managing the client cache Pointer swizzling [11 ] is a well-established tech- in a distributed, p ersistent ob ject storage system. In a companion pap er, we showed that it outp erforms nique to sp eed up p ointer traversals in ob ject- other techniques across a wide range of cache sizes oriented databases. For ob jects in the client cache, and workloads. This rep ort describ es Hac's solution it replaces contained global ob ject names by vir- to a sp eci c problem: how to discard indirection table tual memory p ointers, therebyavoiding the need to entries in an indirect pointer swizzling scheme. Hac uses translate the ob ject name to a p ointer every time a lazy reference counting to solve this problem. Instead reference is used. Pointer swizzling schemes can b e of eagerly up dating reference counts when ob jects are classi ed as direct [13 ] or indirect [11 , 10 ]. In direct mo di ed and eagerly freeing unreferenced entries, which schemes, swizzled p ointers p oint directly to an ob- can b e exp ensive, we p erform these op erations lazily in the background while waiting for replies to fetch and ject, whereas in indirect schemes swizzled p ointers commit requests. Furthermore, weintro duce a number p oint to an indirection table entry that contains of additionaloptimizations to reduce the space and time a p ointer to the ob ject.
    [Show full text]
  • Providing Persistent Objects in Distributed Systems
    Providing Persistent Objects in Distributed Systems Barbara Liskov, Miguel Castro, Liuba Shrira , Atul Adya Laboratory for Computer Science, Massachusetts Institute of Technology, 545 Technology Square, Cambridge, MA 02139 g fliskov,castro,liuba,adya @lcs.mit.edu Abstract. THOR is a persistent object store that provides a powerful programming model. THOR ensures that persistent objects are accessed only by calling their methods and it supports atomic transactions. The result is a system that allows applications to share objects safely across both space and time. The paper describes how the THOR implementation is able to support this pow- erful model and yet achieve good performance, even in a wide-area, large-scale distributed environment. It describes the techniques used in THOR to meet the challenge of providing good performance in spite of the need to manage very large numbers of very small objects. In addition, the paper puts the performance of THOR in perspective by showing that it substantially outperforms a system based on memory mapped files, even though that system provides much less functionality than THOR. 1 Introduction Persistent object stores provide a powerful programmingmodel for modern applications. Objects provide a simple and natural way to model complex data; they are the preferred approach to implementing systems today. A persistent object store decouples the object model from individual programs, effectively providing a persistent heap that can be shared by many different programs: objects in the heap survive and can be used by applications so long as they can be referenced. Such a system can guarantee safe sharing, ensuring that all programs that use a shared object use it in accordance with its type (by calling its methods).
    [Show full text]
  • Address Translation Strategies in the Texas Persistent Store
    THE ADVANCED COMPUTING SYSTEMS ASSOCIATION The following paper was originally published in the 5th USENIX Conference on Object-Oriented Technologies and Systems (COOTS '99) San Diego, California, USA, May 3–7, 1999 Address Translation Strategies in the Texas Persistent Store Sheetal V. Kakkad Somerset Design Center, Motorola Paul R. Wilson University of Texas at Austin © 1999 by The USENIX Association All Rights Reserved Rights to individual papers remain with the author or the author's employer. Permission is granted for noncommercial reproduction of the work for educational or research purposes. This copyright notice must be included in the reproduced paper. USENIX acknowledges all trademarks herein. For more information about the USENIX Association: Phone: 1 510 528 8649 FAX: 1 510 548 5738 Email: [email protected] WWW: http://www.usenix.org Address Translation Strategies in the Texas Persistent Store y Sheetal V. Kakkad Paul R. Wilson Somerset Design Center Department of Computer Sciences Motorola The University of Texas at Austin Austin, Texas, USA Austin, Texas, USA [email protected] [email protected] Abstract Texas using the C++ smart pointer idiom, allowing pro- grammers to choose the kind of pointer used for any data member in a particular class definition. This ap- Texas is a highly portable, high-performance persistent proach maintains the important features of the system: object store that can be used with conventional compil- persistence that is orthogonal to type, high performance ers and operating systems, without the need for a prepro- with standard compilers and operating systems, suitabil- cessor or special operating system privileges. Texas uses ity for huge shared address spaces across heterogeneous pointer swizzling at page fault time as its primary ad- platforms, and the ability to optimize away pointer swiz- dress translation mechanism, translating addresses from zling costs when the persistent store is smaller than the a persistent format into conventional virtual addresses hardware-supported virtual address size.
    [Show full text]
  • Object-Oriented Recovery for Non-Volatile Memory
    CORE Metadata, citation and similar papers at core.ac.uk Provided by Infoscience - École polytechnique fédérale de Lausanne 153 Object-Oriented Recovery for Non-volatile Memory NACHSHON COHEN, EPFL, Switzerland DAVID T. AKSUN, EPFL, Switzerland JAMES R. LARUS, EPFL, Switzerland New non-volatile memory (NVM) technologies enable direct, durable storage of data in an application’s heap. Durable, randomly accessible memory facilitates the construction of applications that do not lose data at system shutdown or power failure. Existing NVM programming frameworks provide mechanisms to consistently capture a running application’s state. They do not, however, fully support object-oriented languages or ensure that the persistent heap is consistent with the environment when the application is restarted. In this paper, we propose a new NVM language extension and runtime system that supports object- oriented NVM programming and avoids the pitfalls of prior approaches. At the heart of our technique is object reconstruction, which transparently restores and reconstructs a persistent object’s state during program restart. It is implemented in NVMReconstruction, a Clang/LLVM extension and runtime library that provides: (i) transient fields in persistent objects, (ii) support for virtual functions and function pointers, (iii) direct representation of persistent pointers as virtual addresses, and (iv) type-specific reconstruction of a persistent object during program restart. In addition, NVMReconstruction supports updating an application’s code, even if this causes objects to expand, by providing object migration. NVMReconstruction also can compact the persistent heap to reduce fragmentation. In experiments, we demonstrate the versatility and usability of object reconstruction and its low runtime performance cost.
    [Show full text]
  • Pointer Swizzling at Page Fault Time ECiently and Compatibly
    Pointer Swizzling at Page Fault Time Eciently and Compatibly Supp orting Huge Address Spaces on Standard Hardware Paul R Wilson and Sheetal V Kakkad Department of Computer Sciences University of Texas Austin Texas wilsoncsutexasedu I could be bounded in a nutshel l and hardware can sp ecify directly Applications of large count myself king of innite space address spaces include distributed shared memories eg Li op erating systems with a single shared W Shakesp eare Hamlet I Iii address space eg CLLBH and p ersistent ob ject + stores eg ABC DSZ Distributed shared memories provide a single address space for appli Abstract cations that span multiple machines shared address space op erating systems provide a single addressing Pointer swizzling at page fault time is a novel mo del for all pro cesses on one or more machines and address translation mechanism that exploits conven p ersistent ob ject stores provide sharable recoverable tional address translation hardware It can supp ort heap storage to eliminate the use of les for most pur huge address spaces eciently without long hardware p oses addresses such large address spaces are attractive for p ersistent ob ject stores distributed shared memories A common feature of these systems is the empha and shared address space op erating systems This sis on simplifying programming by preserving p ointer swizzling scheme can b e used to provide data com semanticsthat is ob ject identity The programmers patibility across machines with dierent word sizes default view of data is of a heap of
    [Show full text]