Heapmon: a Low Overhead, Automatic, and Programmable Memory Bug Detector ∗
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An Efficient Framework for Implementing Persistent Data Structures on Asymmetric NVM Architecture
AsymNVM: An Efficient Framework for Implementing Persistent Data Structures on Asymmetric NVM Architecture Teng Ma Mingxing Zhang Kang Chen∗ [email protected] [email protected] [email protected] Tsinghua University Tsinghua University & Sangfor Tsinghua University Beijing, China Shenzhen, China Beijing, China Zhuo Song Yongwei Wu Xuehai Qian [email protected] [email protected] [email protected] Alibaba Tsinghua University University of Southern California Beijing, China Beijing, China Los Angles, CA Abstract We build AsymNVM framework based on AsymNVM ar- The byte-addressable non-volatile memory (NVM) is a promis- chitecture that implements: 1) high performance persistent ing technology since it simultaneously provides DRAM-like data structure update; 2) NVM data management; 3) con- performance, disk-like capacity, and persistency. The cur- currency control; and 4) crash-consistency and replication. rent NVM deployment with byte-addressability is symmetric, The key idea to remove persistency bottleneck is the use of where NVM devices are directly attached to servers. Due to operation log that reduces stall time due to RDMA writes and the higher density, NVM provides much larger capacity and enables efficient batching and caching in front-end nodes. should be shared among servers. Unfortunately, in the sym- To evaluate performance, we construct eight widely used metric setting, the availability of NVM devices is affected by data structures and two transaction applications based on the specific machine it is attached to. High availability canbe AsymNVM framework. In a 10-node cluster equipped with achieved by replicating data to NVM on a remote machine. real NVM devices, results show that AsymNVM achieves However, it requires full replication of data structure in local similar or better performance compared to the best possible memory — limiting the size of the working set. -
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. -
Rational Purifyplus for UNIX
Develop Fast, Reliable Code Rational PurifyPlus for UNIX Customers and end-users demand that your Automatically Pinpoint code works reliably and offers adequate execu- Hard-To-Find Bugs tion performance. But the reality of your deliv- Reliability problems, such as runtime errors and ery schedule often forces you to sacrifice relia- memory leaks in C/C++, can kill a software bility or performance – sometimes both. And company’s reputation. These errors are hard to without adequate time for testing, you may not find, hard to reproduce, and hardest of all, to even know these issues exist. So you reluctant- explain to customers who discover them. No ly deliver your code before it’s ready, knowing one intentionally relies on their customer as good and well that the integrity of your work their "final QA." But without adequate tools, this may be suspect. is the inevitable outcome. Even the best pro- Rational Software has a solution for ensuring grammers make mistakes in their coding. that your code is both fast and reliable. Rational PurifyPlus for UNIX automatically finds HIGHLIGHTS Rational® PurifyPlus for UNIX combines runtime these reliability errors in C/C++ code that can’t error detection, application performance profil- Automatically finds runtime be found in any other way. It finds the errors ing, and code coverage analysis into a single, errors in C/C++ code even before visible symptoms occur (such as a complete package. Together, these functions system crash or other spurious behavior). And help developers ensure the highest reliability Quickly isolates application it shows you exactly where the error originated, and performance of their software from the very performance bottlenecks regardless of how remote from the visible first release. -
Data Structures Are Ways to Organize Data (Informa- Tion). Examples
CPSC 211 Data Structures & Implementations (c) Texas A&M University [ 1 ] What are Data Structures? Data structures are ways to organize data (informa- tion). Examples: simple variables — primitive types objects — collection of data items of various types arrays — collection of data items of the same type, stored contiguously linked lists — sequence of data items, each one points to the next one Typically, algorithms go with the data structures to manipulate the data (e.g., the methods of a class). This course will cover some more complicated data structures: how to implement them efficiently what they are good for CPSC 211 Data Structures & Implementations (c) Texas A&M University [ 2 ] Abstract Data Types An abstract data type (ADT) defines a state of an object and operations that act on the object, possibly changing the state. Similar to a Java class. This course will cover specifications of several common ADTs pros and cons of different implementations of the ADTs (e.g., array or linked list? sorted or unsorted?) how the ADT can be used to solve other problems CPSC 211 Data Structures & Implementations (c) Texas A&M University [ 3 ] Specific ADTs The ADTs to be studied (and some sample applica- tions) are: stack evaluate arithmetic expressions queue simulate complex systems, such as traffic general list AI systems, including the LISP language tree simple and fast sorting table database applications, with quick look-up CPSC 211 Data Structures & Implementations (c) Texas A&M University [ 4 ] How Does C Fit In? Although data structures are universal (can be imple- mented in any programming language), this course will use Java and C: non-object-oriented parts of Java are based on C C is not object-oriented We will learn how to gain the advantages of data ab- straction and modularity in C, by using self-discipline to achieve what Java forces you to do. -
Software II: Principles of Programming Languages
Software II: Principles of Programming Languages Lecture 6 – Data Types Some Basic Definitions • A data type defines a collection of data objects and a set of predefined operations on those objects • A descriptor is the collection of the attributes of a variable • An object represents an instance of a user- defined (abstract data) type • One design issue for all data types: What operations are defined and how are they specified? Primitive Data Types • Almost all programming languages provide a set of primitive data types • Primitive data types: Those not defined in terms of other data types • Some primitive data types are merely reflections of the hardware • Others require only a little non-hardware support for their implementation The Integer Data Type • Almost always an exact reflection of the hardware so the mapping is trivial • There may be as many as eight different integer types in a language • Java’s signed integer sizes: byte , short , int , long The Floating Point Data Type • Model real numbers, but only as approximations • Languages for scientific use support at least two floating-point types (e.g., float and double ; sometimes more • Usually exactly like the hardware, but not always • IEEE Floating-Point Standard 754 Complex Data Type • Some languages support a complex type, e.g., C99, Fortran, and Python • Each value consists of two floats, the real part and the imaginary part • Literal form real component – (in Fortran: (7, 3) imaginary – (in Python): (7 + 3j) component The Decimal Data Type • For business applications (money) -
Purely Functional Data Structures
Purely Functional Data Structures Chris Okasaki September 1996 CMU-CS-96-177 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Thesis Committee: Peter Lee, Chair Robert Harper Daniel Sleator Robert Tarjan, Princeton University Copyright c 1996 Chris Okasaki This research was sponsored by the Advanced Research Projects Agency (ARPA) under Contract No. F19628- 95-C-0050. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of ARPA or the U.S. Government. Keywords: functional programming, data structures, lazy evaluation, amortization For Maria Abstract When a C programmer needs an efficient data structure for a particular prob- lem, he or she can often simply look one up in any of a number of good text- books or handbooks. Unfortunately, programmers in functional languages such as Standard ML or Haskell do not have this luxury. Although some data struc- tures designed for imperative languages such as C can be quite easily adapted to a functional setting, most cannot, usually because they depend in crucial ways on as- signments, which are disallowed, or at least discouraged, in functional languages. To address this imbalance, we describe several techniques for designing functional data structures, and numerous original data structures based on these techniques, including multiple variations of lists, queues, double-ended queues, and heaps, many supporting more exotic features such as random access or efficient catena- tion. In addition, we expose the fundamental role of lazy evaluation in amortized functional data structures. -
The Developer's Guide to Debugging
The Developer’s Guide to Debugging Thorsten Grotker¨ · Ulrich Holtmann Holger Keding · Markus Wloka The Developer’s Guide to Debugging 123 Thorsten Gr¨otker Ulrich Holtmann Holger Keding Markus Wloka Internet: http://www.debugging-guide.com Email: [email protected] ISBN: 978-1-4020-5539-3 e-ISBN: 978-1-4020-5540-9 Library of Congress Control Number: 2008929566 c 2008 Springer Science+Business Media B.V. No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Printed on acid-free paper 987654321 springer.com Foreword Of all activities in software development, debugging is probably the one that is hated most. It is guilt-ridden because a technical failure suggests personal fail- ure; because it points the finger at us showing us that we have been wrong. It is time-consuming because we have to rethink every single assumption, every single step from requirements to implementation. Its worst feature though may be that it is unpredictable: You never know how much time it will take you to fix a bug - and whether you’ll be able to fix it at all. Ask a developer for the worst moments in life, and many of them will be related to debugging. It may be 11pm, you’re still working on it, you are just stepping through the program, and that’s when your spouse calls you and asks you when you’ll finally, finally get home, and you try to end the call as soon as possible as you’re losing grip on the carefully memorized observations and deductions. -
Reducing the Storage Overhead of Main-Memory OLTP Databases with Hybrid Indexes
Reducing the Storage Overhead of Main-Memory OLTP Databases with Hybrid Indexes Huanchen Zhang David G. Andersen Andrew Pavlo Carnegie Mellon University Carnegie Mellon University Carnegie Mellon University [email protected] [email protected] [email protected] Michael Kaminsky Lin Ma Rui Shen Intel Labs Carnegie Mellon University VoltDB [email protected] [email protected] [email protected] ABSTRACT keep more data resident in memory. Higher memory efficiency al- Using indexes for query execution is crucial for achieving high per- lows for a larger working set, which enables the system to achieve formance in modern on-line transaction processing databases. For higher performance with the same hardware. a main-memory database, however, these indexes consume a large Index data structures consume a large portion of the database’s fraction of the total memory available and are thus a major source total memory—particularly in OLTP databases, where tuples are of storage overhead of in-memory databases. To reduce this over- relatively small and tables can have many indexes. For example, head, we propose using a two-stage index: The first stage ingests when running TPC-C on H-Store [33], a state-of-the-art in-memory all incoming entries and is kept small for fast read and write opera- DBMS, indexes consume around 55% of the total memory. Freeing tions. The index periodically migrates entries from the first stage to up that memory can lead to the above-mentioned benefits: lower the second, which uses a more compact, read-optimized data struc- costs and/or the ability to store additional data. -
Consistent Overhead Byte Stuffing
Consistent Overhead Byte Stuffing Stuart Cheshire and Mary Baker Computer Science Department, Stanford University Stanford, California 94305, USA {cheshire,mgbaker}@cs.stanford.edu ted packet. In general, some added overhead (in terms Abstract of additional bytes transmitted over the serial me- dium) is inevitable if we are to perform byte stuffing Byte stuffing is a process that transforms a sequence of data without loss of information. bytes that may contain ‘illegal’ or ‘reserved’ values into a potentially longer sequence that contains no occurrences of Current byte stuffing algorithms, such as those used those values. The extra length is referred to in this paper as by SLIP [RFC1055], PPP [RFC1662] and AX.25 the overhead of the algorithm. [ARRL84], have potentially high variance in per- packet overhead. For example, using PPP byte To date, byte stuffing algorithms, such as those used by stuffing, the overall overhead (averaged over a large SLIP [RFC1055], PPP [RFC1662] and AX.25 [ARRL84], number of packets) is typically 1% or less, but indi- have been designed to incur low average overhead but have vidual packets can increase in size by as much as made little effort to minimize worst case overhead. 100%. Some increasingly popular network devices, however, care While this variability in overhead may add some jitter more about the worst case. For example, the transmission and unpredictability to network behavior, it has only time for ISM-band packet radio transmitters is strictly minor implications for running PPP over a telephone limited by FCC regulation. To adhere to this regulation, the modem. The IP host using the PPP protocol has only practice is to set the maximum packet size artificially low so to make its transmit and receive serial buffers twice as that no packet, even after worst case overhead, can exceed big as the largest IP packet it expects to send or the transmission time limit. -
Debugging and Tuning Linux for EDA
Debugging and Tuning Linux for EDA Fabio Somenzi [email protected] University of Colorado at Boulder Outline Compiling gcc icc/ecc Debugging valgrind purify ddd Profiling gcov, gprof quantify vtl valgrind Compiling Compiler options related to static checks debugging optimization Profiling-driven optimization Compiling with GCC gcc -Wall -O3 -g reports most uses of potentially uninitialized variables -O3 (or -O6) necessary to trigger dataflow analysis can be fooled by if (cond) x = VALUE; ... if (cond) y = x; Uninitialized variables not considered for register allocation may escape Achieving -Wall-clean code is not too painful and highly desirable Compiling C code with g++ is more painful, but has its rewards Compiling with GCC gcc -mcpu=pentium4 -malign-double -mcpu=pentium4 optimizes for the Pentium 4, but produces code that runs on any x86 -march=pentium4 uses Pentium 4-specific instructions -malign-double forces alignment of double’s to double-word boundary Use either for all files or for none gcc -mfpmath=sse Controls the use of SSE instructions for floating point For complete listing, check gcc’s info page under Invoking gcc ! Submodel Options Compiling with ICC ICC is the Intel compiler for IA-32 systems. http://www.intel.com/software/products/ icc -O3 -g -ansi -w2 -Wall Aggressive optimization Retain debugging info Strict ANSI conformance Display remarks, warnings, and errors Enable all warnings Remarks tend to be a bit overwhelming Fine grain control over diagnostic: see man page Compiling with ICC icc -tpp7 Optimize instruction scheduling -
Memory Analysis What Does Percolation Model?
Class Meeting #2 COS 226 — Spring 2018 Based on slides by Jérémie Lumbroso — Motivation — Problem description — API — Backwash — Empirical Analysis — Memory Analysis What does Percolation model? Other examples: • Water freezing • Ferromagnetic effects Create private “helper” funtion private int getIntFromCoord(int row, int col) { return N * row + col; } Or perhaps since this function will be used a lot, should it have a shorter name? For ex.: site or location or cell or grid, etc., … public class UF initialize union-find data structure with UF(int N) what you must do N objects (0 to N – 1) void union(int p,int q) add connection between p and q both are APIs boolean connected(int p,int q) are p and q in the same component? what is int find(int p) component identifier for p (0 to N – 1) provided int count() number of components Why an API? API = Application Programming Interface —a contract between a programmers —be able to know about the functionality without details from the implementation open union PercolationStats Percolation UF percolates connected Each of these modules could be programmed by anybody / implemented anyway Example 1: Car public class Car { void turnLeft() void turnRight() void shift(int gear) void break() } A. Electrical? B. Hybrid? C. Gasoline? D. Diesel? E. Hydrogen cell? Example 1: Car public class Car { void turnLeft() void turnRight() void shift(int gear) void break() } Example 2: Electrical Outlets original API API with added public members is incompatible with rest of the clients Why is it so important to implement the prescribed API? Writing to an API is an important skill to master because it is an essential component of modular programming, whether you are developing software by yourself or as part of a group. -
Optimizing Data Structures in High-Level Programs New Directions for Extensible Compilers Based on Staging
Optimizing Data Structures in High-Level Programs New Directions for Extensible Compilers based on Staging Tiark Rompf ∗‡ Arvind K. Sujeeth† Nada Amin∗ Kevin J. Brown† Vojin Jovanovic∗ HyoukJoong Lee† Manohar Jonnalagedda∗ Kunle Olukotun† Martin Odersky∗ ∗EPFL: {first.last}@epfl.ch ‡Oracle Labs †Stanford University: {asujeeth, kjbrown, hyouklee, kunle}@stanford.edu Abstract // Vectors object Vector { High level data structures are a cornerstone of modern program- def fromArray[T:Numeric](a: Array[T]) = ming and at the same time stand in the way of compiler optimiza- new Vector { val data = a } tions. In order to reason about user or library-defined data struc- def zeros[T:Numeric](n: Int) = tures, compilers need to be extensible. Common mechanisms to Vector.fromArray(Array.fill(n)(i => zero[T])) extend compilers fall into two categories. Frontend macros, stag- } ing or partial evaluation systems can be used to programmatically abstract class Vector[T:Numeric] { remove abstraction and specialize programs before they enter the val data: Array[T] compiler. Alternatively, some compilers allow extending the in- def +(that: Vector[T]) = ternal workings by adding new transformation passes at different Vector.fromArray(data.zipWith(that.data)(_ + _)) ... points in the compile chain or adding new intermediate represen- } tation (IR) types. None of these mechanisms alone is sufficient to // Matrices handle the challenges posed by high level data structures. This pa- abstract class Matrix[T:Numeric] { ... } per shows a novel way to combine them to yield benefits that are // Complex Numbers greater than the sum of the parts. case class Complex(re: Double, im: Double) { Instead of using staging merely as a front end, we implement in- def +(that: Complex) = Complex(re + that.re, im + that.im) ternal compiler passes using staging as well.