DATABASE CONCURRENCY CONTROL USING TRANSACTIONAL MEMORY : PERFORMANCE EVALUATION

Size: px
Start display at page:

Download "DATABASE CONCURRENCY CONTROL USING TRANSACTIONAL MEMORY : PERFORMANCE EVALUATION"

Transcription

1 DATABASE CONCURRENCY CONTROL USING TRANSACTIONAL MEMORY : PERFORMANCE EVALUATION Jeong Seung Yu a, Woon Hak Kang b, Hwan Soo Han c and Sang Won Lee d School of Info. & Comm. Engr. Sungkyunkwan University Suwon , Korea Altibase Corp , Guro dong, Guro Gu Seoul, , Korea { a tinyisle, b woonagi319, c hhan, d swlee }@skku.edu ABSTRACT Recently, multi core processors are widely used. In multi core environment, programs should be parallelized to maximize utilization of the processor. In parallel programming, locking method has been used for concurrency control. And most of current database systems also use locking method. Concurrency control scheme based on locking method has not been parallelized due to a locking overhead. It is one of the most obstacles to system performance in multi core environment. Transactional memory has been proposed as an alternative. It provides lower overhead and more parallelism than locking method. In this paper, we evaluate the performance of concurrency control schemes using transactional memory, optimistic concurrency control and locking method respectively. Our experimental results show that transactional memory is up to 9 times faster than the others and it is more effective in OLTP environment which mainly has short and read-intensive transactions. K ey words Database, Concurrency Control, Optimistic Concurrency Control, Transactional Memory, Multicore 1. INTRODUCTION In the past, processor development focused on finding a singlecore processor which had faster speed and higher clock. However, recent movement of processor development is not making a singlecore processor, but is changed to make a multicore processor which contains many cores on one chip. This movement promotes the change of software development. Increasing clock speed has resulted in higher speed of application programs so far. But application programs cannot get free lunch anymore as processor development focuses on making much more cores rather than clock speed. In multicore environment, application programs have to be designed to utilize parallelism efficiently. Previous sequence programs cannot use cores in a multi core processor, and it results in decreased total throughput. Assume the case when several threads access to shared resource in parallel programs. Indiscreet access to shared resource without control method can cause incorrect outcome. Traditionally, locking method such as mutex and semaphore is used as a solution to prevent this problem. In locking method, shared resource can be accessed only if thread has a lock. Otherwise it is impossible to access to shared resource and it is necessary to wait until getting a lock. Moreover, each access to shared resource gets a lock, and overhead to maintain is very heavy in locking method. These properties make processor utilization less effective. Transactional memory was introduced to solve these weak points of locking method. Transactional memory is concurrency control method that guarantees atomic execution for operation sequence called transaction. Transactional memory does not have overhead which locking method has in order to get and manage lock. If simultaneous accesses for shard memory do not cause any problems, first, overhead caused by a lock is waste. Also, Transactional memory which does not have this overhead caused by a lock has better performance than locking method. For better process, database system also allows many transactions to access to shared data at once. Furthermore, locking method has been traditionally used as concurrency control method to control transactions which access simultaneously. Overhead caused by a lock is main reason for decreasing system performance in programs, such as database system, which has many parallel accesses to shared data. In parallel programming field, concurrency control in database system can be implemented by using transactional memory which is suggested to overcome locking method. This can improve processor utilization in multicore environment. The paper is organized as follows. Section 2 deals with locking method, optimistic concurrency control in database, and transactional memory in parallel programming. Section 3 discusses optimistic concurrency control implementation using transactional memory. Finally, Section 4 contains conclusion and future research. 2. BACKGROUND This section explains typical concurrency control methods of database and parallel programming. Locking method and optimistic concurrency control in database systems will be explained first and then transactional memory in parallel programming will be the next. 2.1 Locking method Locking method is the most popular concurrency control method in data base system. In locking method, all transactions have to get an appropriate lock before accessing shared data. If not, transactions have to wait until getting a lock. Lock is separated into two classes, shard for reading and exclusive

2 lock reading and writing. Shared lock can be gotten with the shared lock which is held by other transactions at once, on the other hand, exclusive lock can be only gotten by one transaction at a time. Locking method has several weak points. First, it is lock management overhead. Not only the overhead searching a node to get a lock but also other overhead, such as holding, converting, and releasing a lock, are big. Another problem is lock waiting overhead when requested lock is not compatible. Waiting to get a lock has negative effects on overall throughput and response time. The most serious problem is deadlock. Deadlock occurs when one transaction holding a lock waits for another lock held by another transaction. For example, if transaction A waits for a lock held by transaction B, and transaction B waits for a lock held by transaction A, both transactions cannot proceed. Both ones will keep waiting unlimitedly. To solve this problem, one transaction has to be aborted and all locks hold by that transaction has to be released. If many transactions run simultaneously, these problems become more serious. 2.2 Optimistic Concurrency Control Another concurrency control method in database field is Optimistic Concurrency Control [1]. It was introduced to supplement the week points of locking method. In locking method, the basic premise is that transactions running concurrently cause problems with consistency. Therefore, lock is used to prevent problems. In optimistic concurrency control, on contrast, the basic premise is that transactions running concurrently do not cause consistency problems in most cases. As consistency problems do not occur, the lock which causes overhead is not necessary. Optimistic concurrency control does not have overhead for lock management and deadlock because it does not use any lock. In optimistic concurrency control, transactions proceed in three phases; read, validation, and write phase. In read phase, the transaction executes all operations. Information on data set which transaction creates, deletes, reads, and writes is kept. In addition, created or updated operation outcomes are kept in a local area only for certain transaction. In validation phase, the transaction performs a validation test to see whether consistency problems occur or not. If transaction succeeds in validation, it performs write phase. If transaction fails in validation, transaction is aborted and restarted. In write phase, new values which are kept in a local area are spread to a global area. It means new values are global data, so other transactions can access to them. Optimistic concurrency control has better overall throughput and response time than locking method because it does not have overhead by using a lock. However, it has some disadvantages as well. First, while it does not have deadlock, it has starvation problem. When one transaction is aborted continually by another transaction, it is called starvation. It can causes bad response time. In this situation, one transaction which conflicts with the other transaction needs to be temporarily blocked so that the other transaction can be finished. Another problem is that performance depends on transaction length and write operation rate. Because the transaction which has short running time or is read intensive has lower possibility to conflict, it works well. The opposite is also true. The transaction which has long running time or many writing operation has more chance to conflict does not work well with many returning and restarting. 2.3 Transactional Memory In parallel programming field, as well as database system, if several threads access to the same shared memory, the synchronization scheme is needed. In most cases, locking method such as mutex and semaphore is widely used, like database system. Locking method in parallel programming has problems with deadlock or managing and waiting for a lock like locking method in database system. Another problem with locking method is that it is difficult to design, debug, and maintain program. The more locks are used, the more this overhead is increased. Transactional memory was introduced by Lomet [2] in order to avoid overhead caused by using a lock. It was first implemented by Herlihy and Moss [3]. Transaction memory came from the concept of transaction in database. Transactional Memory ensures atomicity and isolation among four properties of database transaction; atomicity, consistency, isolation, durability. Atomicity makes sure overall state changes of operations in a transaction. In other words, overall operations of a transaction appropriately finish being executed or not at all. Isolation means even if several transactions are executed concurrently, operation result of each transaction cannot affect another transaction operation. If confliction occurs between transactions which have different result from each other, transactional memory aborts execution results and restart to ensure atomicity. By ensuring that result of transaction executions is invisible to another transactions before it is completed, transactional memory guarantees isolation. This property is very similar to optimistic concurrency control in the database. Transactional memory is separated into hardware transactional memory [3, 4, 5, 6] and software transactional memory [7, 8, 9] based on its execution. Hardware transactional memory cannot apply to a transaction which exceeds cache size because transaction keeps the information about accessed data into processor cache. However, it has better performance than software transactional memory. Conversely, software transactional memory is slower than hardware transactional memory, but it is independent of hardware. Conflict Lazy Eager Version Management Lazy Eager OCC DBMSs[1] none Stanford TCC[5] MIT LTM[4] CCC DBMSs[1] Intel/Brown VTM[6] MIT UTM[4] (on cache conflicts) LogTM[11] Table 1: A Transactional Memory (TM) taxonomy Table 1 illustrates which transactional memory proposals use lazy versus eager management and conflict detection. Eager confliction detection is when confliction detection is executed at the point of read or write operation. If confliction detection is executed later, not at read or write operation but

3 complete point, it is lazy one. Eager version management is when new values are stored in place directly. If new values are stored in some place temporarily and then stored again in a right place later, it is lazy one. 3. CONCURRENCY CONTROL IMPLEMENTATION USING TRANSACTIONAL MEMORY 3.2 Performance on Singlecore For single core environment, only one core among four cores is enabled. Each transaction executes read or write operation for 1 records and each operation reads or writes a data which is 1 word size. The records to be accessed are selected randomly in the entire database. 5 Transactional memory is essentially similar to optimistic concurrency control. Both methods hope that most parallel accesses do not make any problems. Moreover, both methods do not prevent conflict before accessing to such as locking method, but both detect and handle conflict after accessing. These similarities provide the possibility to develop optimistic concurrency control in database system by using transactional memory. We can use transactional memory as a substitute for version management and conflict detection function in optimistic concurrency control. Furthermore, transactional memory has strong points of performance as well as convenience of program development. As previously mentioned, transactional memory can help to make the best use of multi core than locking method. Therefore, optimistic concurrency control using transactional memory can make better performance than locking method. 3.1 Evaluation Setup The computer used in the experiments has Intel i GHz Quad-Core processor and 4GB ram. That processor has four cores and support hyper-threading. We assume that the database in our experiments is OLTP database which is accessed by many transactions concurrently. Each record is 64byte, and one page consists of 64 records. The entire database consists of a single table which has 25, pages, and all data are main memory database management system stored in main memory. We ran 5 threads concurrently with 25, transactions which executes read or write operation to accessed records. In the experiments, Intel C++ Compiler Prototype Edition 3. [12], which is one of the software transactional memory, is used. Although it is slower than hardware transactional memory, it supports transactional memory with compiler level and does not have hardware limit. It identifies codes defined as the keyword, tm atomic and ensures atomic execution by tracking a conflict in 64byte memory address. We use optimistic concurrency control method introduced by H.T Kung and Jone T. Robinson. However, consider only read and write operation. In locking method, we use a record-level locking and two locking modes, such as shared lock and exclusive lock. The records which will be accessed by transaction are sorted based on the record identifier order in advance. When transactions access to a record, transactions acquire a lock in order from low record id to high record id and release a lock in reverse order Transactional Memory Optimistic Concurrency Control Locking Method Figure 1: Execution time for short transactions Figure 1 shows the performance of each concurrency control in single core. X-axis is the rate of read operation among 1 times access to record, and Y-axis is execution time. All concurrency control methods show similar execution time for the case of read operation only and write operation only. Locking method takes approximately 45 seconds for read operation only. It means locking method has huge overhead for lock management Transaction memory and optimistic concurrency control are much faster than locking method. The reason is that transactional memory and optimistic concurrency control are strong in short transactions. Figure 2(a), 2(b), and 2(c) show the result of the performance when the number of record accessed by transactions is changed. If the number is increased, transaction operations which make execution time increased are increased. Figure 2(a) and 2(b) show that execution time for both transactional memory and optimistic concurrency control will be increased as the number of record rises. Figure 2(c) shows that, however, there will not be big change of execution time in locking method even if the number of record rises. It shows overhead caused by lock management is much heavier than overhead caused by read or write operations in locking method. We executed each method under heavier computation condition. In this case, one write operation executes computations 64 times. Figure 3 shows the result of the performance of both transactional memory and optimistic concurrency control when each transaction accesses to 5 records. Execution time of both is increased more than the past experiment. The reason is that increased execution time by executing 64-time computations causes more conflict between transactions. In locking method, the result is similar to figure 2(c). It means that lock management time has more effects on the entire performance than computation time

4 .3 TM 5 Records TM 3 Records TM 1 Records Transactional Memory OCC (a) Transactional Memory Figure 3: Execution time on heavy computation Figure 2: length 1 OCC 5 Records OCC 3 Records OCC 1 Records (b) Optimistic Concurrency Control Lock 5 Records Lock 3 Records Lock 1 Records (c) Locking Method Execution time on various transaction Performance on Multicore The number of core enabled in multi core environment is changed for performance evaluation. Each transaction accesses to 1 records which executes read or write operation, and each operation contains heavy computations. The accessed records are randomly selected in the entire database. Figure 4 is the result based of the change of the number of enabled core from 1 to 2, 4. Figure 4(a) shows that locking method gets just 1.4% improved performance in 4 cores compared with 1 core. The reason is that locking method has huge overhead for lock management and has long critical section in the program. Long critical section can enable only one core to work can at once even if processor has several cores. This is why locking method cannot get highly improved performance in multi core environment. Figure 4(b) shows that optimistic concurrency control gets performance gain up to 35% in 4 cores. And Figure 4(c) shows that transactional memory gets performance gain up to 71% in 4 cores. Transactional memory and optimistic concurrency control need to compete with other transactions only when the transactions want to execute conflict detection before completion. It also takes a very short time. This helps both transactional memory and optimistic concurrency control to utilize more cores and get better performance gain than locking method in multi core environment. We experiments very long transactions which access to 4 records and execute heavy computation for each write operations under 4 cores. Figure 5 shows the results of each concurrency control method. Execution time for locking method takes longer than it does in Figure 4(c) which transactions access to 1 records, but the difference is up to 15 seconds. Optimistic concurrency control goes very fast for the case of read operation only, but it is 8 times slower than Figure 4(b) and locking method under other conditions. Transactional memory is also slower than result of previous experiment. And it is slower than locking method when the rate of write operations is over 6%. For very long transactions, transactional memory and optimistic concurrency control cause many restarts due to transaction conflicts. Thus locking method is better

5 5 4 TM 1 Core TM 2 Core TM 4 Core Transactional Memory Optimistic Concurrency Control Locking Method (a) Transactional Memory Figure 5: Execution time for long transations 16 OCC 1 Core OCC 2 Core OCC 4 Core than other methods in the case of long transactions CONCLUSION (b) Optimistic Concurrency Control Lock 1 Core Lock 2 Core Lock 4 Core We found that concurrency control using transactional memory is better than locking method and optimistic concurrency control in multi core environment. Previous locking method has limitation in multi core environment. Even if the number of cores contained in processor is increased, locking method cannot use processor power appropriately because of the lock management overhead. Thus we need new concurrency control method which can fully utilize cores contained in processor, and either transactional memory or optimistic concurrency control can be a good example. Both methods show good performance for the transactions which is short and contains more read operations than write operations. Especially, transactional memory shows really good performance in multi core environment. In OLTP environment, many transactions simultaneously request operations and, the requested operations usually access to only a few records. Concurrency control using transactional memory is better than locking method in this situation. In addition, if the purpose of future processor development is to make multi core which contains more cores, transactional memory is much better than locking method. However, transactional memory has been still studied and needs to be supplemented. To get better performance, we need transactional memory which is supported or implemented by hardware. Moreover, it is necessary to consider disk access and handling long transactions so that transactional memory can be apply to database systems based on disk (c) Locking Method Figure 4: Execution time in Multi core ACKNOWLEDGMENT This work was supported in part by MKE, Korea under ITRC NIPA-21-(C ), Seoul Metropolitan Government Seoul R&BD Program (PA993) and MEST, Korea under NRF Grant (NRF ).

6 5. REFERENCES [1] H. T. Kung and J. T. Robinson. On optimistic methods for concurrency control. In ACM Transactions on DAtabase Systems, pages , [2] D. B. Lomet. Process structuring, synchronization and recovery using atomic actions. In Proc. ACM Conf on Language Design For Reliable Software, pages , [3] M. Herlihy and J. E. B. Moss. Transactional memory: Architectural support for lock-free data structures. In Proc. of the 2th Annual Intl. Symp. on Computer Architecture, pages 289 3, [4] C. S. Ananian, Krste Asanovic, Bradley C. Kuszmaul, Charles E. Leiserson, and Sean Lie. Unbounded transactional memory. In Proc. of the Eleventh IEEE Sump. on High-performance Computer Architecture, 25. [5] Lance Hammond, Vicky Wong, Mike Chen, Brian D. Carlstrom, John D. Davis, Ben Hertzberg, Manohar K. Prabhu, Honggo Wijaya, Christos Kozyrakis, and Kunle Olukotun. Transactional memory coherence and consistency. In Proc. of the 31st Annual Intl. Symp. on Computer Architecture, 24. [6] Ravi Rajwar, Maurice Herlihy,, and Konrad Lai. Virtualizing transactional memory. In Proc. of the 32nd Annual Intl. Sump. on computer Architecture, 25. [7] Tim Harris and Keir Fraser. Language support for lightweight transacitons. In Proc. of the 18th SIGPLAN Conference on Object-Oriented Programming, Systems, Languages and Application(OOPSLA), 23. [8] Maurice Herlihy, Victor Luchangco, Mark Moir, and William Scherer III. Software transctional memory for dynamic sized data structures. In Twenty=Second ACM Symp. on Principles of Distributed Computing, 23. [9] Nir Shavit and Dan Touitou. Software transactional memory. In Fourteenth ACM Symp. on Principles of Distributed computing, [1] K. P. Eswaran, J. N. Gray, R. A. Lorie, and I. L. Traiger. The notion of consistency and predicate locks in a database system. Communications of the ACM, 19(11): , [11] K. Moore, J. Bobba, M. Moravan, M. Hill, and D. Wood. Logtm: log-based transacional memory. In Proc. of the Twelfth IEEE Symp. on High-Performance Computer Architecture, pages , 26. [12] Intel Corp. Intel C++ STM Compiler, Prototype Edition 3..

Transactional Memory

Transactional Memory Transactional Memory Konrad Lai Microprocessor Technology Labs, Intel Intel Multicore University Research Conference Dec 8, 2005 Motivation Multiple cores face a serious programmability problem Writing

More information

LogTM: Log-based Transactional Memory

LogTM: Log-based Transactional Memory Appears in the proceedings of the 12th Annual International Symposium on High Performance Computer Architecture (HPCA-12) Austin, TX February 11-15, 2006 LogTM: Log-based Transactional Memory Kevin E.

More information

Energy Efficiency of Software Transactional Memory in a Heterogeneous Architecture

Energy Efficiency of Software Transactional Memory in a Heterogeneous Architecture Energy Efficiency of Software Transactional Memory in a Heterogeneous Architecture Emilio Villegas, Alejandro Villegas, Angeles Navarro, Rafael Asenjo, Yash Ukidave, Oscar Plata University of Malaga, Dept.

More information

Challenges for synchronization and scalability on manycore: a Software Transactional Memory approach

Challenges for synchronization and scalability on manycore: a Software Transactional Memory approach Challenges for synchronization and scalability on manycore: a Software Transactional Memory approach Maurício Lima Pilla André Rauber Du Bois Adenauer Correa Yamin Ana Marilza Pernas Fleischmann Gerson

More information

Database Concurrency Control and Recovery. Simple database model

Database Concurrency Control and Recovery. Simple database model Database Concurrency Control and Recovery Pessimistic concurrency control Two-phase locking (2PL) and Strict 2PL Timestamp ordering (TSO) and Strict TSO Optimistic concurrency control (OCC) definition

More information

Why Computers Are Getting Slower (and what we can do about it) Rik van Riel Sr. Software Engineer, Red Hat

Why Computers Are Getting Slower (and what we can do about it) Rik van Riel Sr. Software Engineer, Red Hat Why Computers Are Getting Slower (and what we can do about it) Rik van Riel Sr. Software Engineer, Red Hat Why Computers Are Getting Slower The traditional approach better performance Why computers are

More information

Course Development of Programming for General-Purpose Multicore Processors

Course Development of Programming for General-Purpose Multicore Processors Course Development of Programming for General-Purpose Multicore Processors Wei Zhang Department of Electrical and Computer Engineering Virginia Commonwealth University Richmond, VA 23284 wzhang4@vcu.edu

More information

SEER PROBABILISTIC SCHEDULING FOR COMMODITY HARDWARE TRANSACTIONAL MEMORY. 27 th Symposium on Parallel Architectures and Algorithms

SEER PROBABILISTIC SCHEDULING FOR COMMODITY HARDWARE TRANSACTIONAL MEMORY. 27 th Symposium on Parallel Architectures and Algorithms 27 th Symposium on Parallel Architectures and Algorithms SEER PROBABILISTIC SCHEDULING FOR COMMODITY HARDWARE TRANSACTIONAL MEMORY Nuno Diegues, Paolo Romano and Stoyan Garbatov Seer: Scheduling for Commodity

More information

Versioned Transactional Shared Memory for the

Versioned Transactional Shared Memory for the Versioned Transactional Shared Memory for the FénixEDU Web Application Nuno Carvalho INESC-ID/IST nonius@gsd.inesc-id.pt João Cachopo INESC-ID/IST joao.cachopo@inesc-id.pt António Rito Silva INESC-ID/IST

More information

Dynamic resource management for energy saving in the cloud computing environment

Dynamic resource management for energy saving in the cloud computing environment Dynamic resource management for energy saving in the cloud computing environment Liang-Teh Lee, Kang-Yuan Liu, and Hui-Yang Huang Department of Computer Science and Engineering, Tatung University, Taiwan

More information

Achieving Nanosecond Latency Between Applications with IPC Shared Memory Messaging

Achieving Nanosecond Latency Between Applications with IPC Shared Memory Messaging Achieving Nanosecond Latency Between Applications with IPC Shared Memory Messaging In some markets and scenarios where competitive advantage is all about speed, speed is measured in micro- and even nano-seconds.

More information

SAP HANA - Main Memory Technology: A Challenge for Development of Business Applications. Jürgen Primsch, SAP AG July 2011

SAP HANA - Main Memory Technology: A Challenge for Development of Business Applications. Jürgen Primsch, SAP AG July 2011 SAP HANA - Main Memory Technology: A Challenge for Development of Business Applications Jürgen Primsch, SAP AG July 2011 Why In-Memory? Information at the Speed of Thought Imagine access to business data,

More information

Introduction to Cloud Computing

Introduction to Cloud Computing Introduction to Cloud Computing Parallel Processing I 15 319, spring 2010 7 th Lecture, Feb 2 nd Majd F. Sakr Lecture Motivation Concurrency and why? Different flavors of parallel computing Get the basic

More information

Performance Analysis of Web based Applications on Single and Multi Core Servers

Performance Analysis of Web based Applications on Single and Multi Core Servers Performance Analysis of Web based Applications on Single and Multi Core Servers Gitika Khare, Diptikant Pathy, Alpana Rajan, Alok Jain, Anil Rawat Raja Ramanna Centre for Advanced Technology Department

More information

Parallel Processing and Software Performance. Lukáš Marek

Parallel Processing and Software Performance. Lukáš Marek Parallel Processing and Software Performance Lukáš Marek DISTRIBUTED SYSTEMS RESEARCH GROUP http://dsrg.mff.cuni.cz CHARLES UNIVERSITY PRAGUE Faculty of Mathematics and Physics Benchmarking in parallel

More information

GPU File System Encryption Kartik Kulkarni and Eugene Linkov

GPU File System Encryption Kartik Kulkarni and Eugene Linkov GPU File System Encryption Kartik Kulkarni and Eugene Linkov 5/10/2012 SUMMARY. We implemented a file system that encrypts and decrypts files. The implementation uses the AES algorithm computed through

More information

Using Logs to Increase Availability in Real-Time. Tiina Niklander and Kimmo Raatikainen. University of Helsinki, Department of Computer Science

Using Logs to Increase Availability in Real-Time. Tiina Niklander and Kimmo Raatikainen. University of Helsinki, Department of Computer Science Using Logs to Increase Availability in Real-Time Main-Memory Tiina Niklander and Kimmo Raatikainen University of Helsinki, Department of Computer Science P.O. Box 26(Teollisuuskatu 23), FIN-14 University

More information

Software and the Concurrency Revolution

Software and the Concurrency Revolution Software and the Concurrency Revolution A: The world s fastest supercomputer, with up to 4 processors, 128MB RAM, 942 MFLOPS (peak). 2 Q: What is a 1984 Cray X-MP? (Or a fractional 2005 vintage Xbox )

More information

Task Scheduling in Speculative Parallelization

Task Scheduling in Speculative Parallelization Task Scheduling in Speculative Parallelization David Baptista Instituto Superior Técnico Universidade Técnica de Lisboa Av. Rovisco Pais, 1 1049-001 Lisboa Portugal david.baptista@ist.utl.pt ABSTRACT Concurrent

More information

Applying Attribute Level Locking to Decrease the Deadlock on Distributed Database

Applying Attribute Level Locking to Decrease the Deadlock on Distributed Database Applying Attribute Level Locking to Decrease the Deadlock on Distributed Database Dr. Khaled S. Maabreh* and Prof. Dr. Alaa Al-Hamami** * Faculty of Science and Information Technology, Zarqa University,

More information

Udai Shankar 2 Deptt. of Computer Sc. & Engineering Madan Mohan Malaviya Engineering College, Gorakhpur, India

Udai Shankar 2 Deptt. of Computer Sc. & Engineering Madan Mohan Malaviya Engineering College, Gorakhpur, India A Protocol for Concurrency Control in Real-Time Replicated Databases System Ashish Srivastava 1 College, Gorakhpur. India Udai Shankar 2 College, Gorakhpur, India Sanjay Kumar Tiwari 3 College, Gorakhpur,

More information

Accelerating Enterprise Applications and Reducing TCO with SanDisk ZetaScale Software

Accelerating Enterprise Applications and Reducing TCO with SanDisk ZetaScale Software WHITEPAPER Accelerating Enterprise Applications and Reducing TCO with SanDisk ZetaScale Software SanDisk ZetaScale software unlocks the full benefits of flash for In-Memory Compute and NoSQL applications

More information

FPGA-based Multithreading for In-Memory Hash Joins

FPGA-based Multithreading for In-Memory Hash Joins FPGA-based Multithreading for In-Memory Hash Joins Robert J. Halstead, Ildar Absalyamov, Walid A. Najjar, Vassilis J. Tsotras University of California, Riverside Outline Background What are FPGAs Multithreaded

More information

IncidentMonitor Server Specification Datasheet

IncidentMonitor Server Specification Datasheet IncidentMonitor Server Specification Datasheet Prepared by Monitor 24-7 Inc October 1, 2015 Contact details: sales@monitor24-7.com North America: +1 416 410.2716 / +1 866 364.2757 Europe: +31 088 008.4600

More information

What is RAID? data reliability with performance

What is RAID? data reliability with performance What is RAID? RAID is the use of multiple disks and data distribution techniques to get better Resilience and/or Performance RAID stands for: Redundant Array of Inexpensive / Independent Disks RAID can

More information

System Copy GT Manual 1.8 Last update: 2015/07/13 Basis Technologies

System Copy GT Manual 1.8 Last update: 2015/07/13 Basis Technologies System Copy GT Manual 1.8 Last update: 2015/07/13 Basis Technologies Table of Contents Introduction... 1 Prerequisites... 2 Executing System Copy GT... 3 Program Parameters / Selection Screen... 4 Technical

More information

Boost SQL Server Performance Buffer Pool Extensions & Delayed Durability

Boost SQL Server Performance Buffer Pool Extensions & Delayed Durability Boost SQL Server Performance Buffer Pool Extensions & Delayed Durability Manohar Punna President - SQLServerGeeks #509 Brisbane 2016 Agenda SQL Server Memory Buffer Pool Extensions Delayed Durability Analysis

More information

Enhancing SQL Server Performance

Enhancing SQL Server Performance Enhancing SQL Server Performance Bradley Ball, Jason Strate and Roger Wolter In the ever-evolving data world, improving database performance is a constant challenge for administrators. End user satisfaction

More information

Chapter 6, The Operating System Machine Level

Chapter 6, The Operating System Machine Level Chapter 6, The Operating System Machine Level 6.1 Virtual Memory 6.2 Virtual I/O Instructions 6.3 Virtual Instructions For Parallel Processing 6.4 Example Operating Systems 6.5 Summary Virtual Memory General

More information

A Dynamic Resource Management with Energy Saving Mechanism for Supporting Cloud Computing

A Dynamic Resource Management with Energy Saving Mechanism for Supporting Cloud Computing A Dynamic Resource Management with Energy Saving Mechanism for Supporting Cloud Computing Liang-Teh Lee, Kang-Yuan Liu, Hui-Yang Huang and Chia-Ying Tseng Department of Computer Science and Engineering,

More information

Processor Architectures

Processor Architectures ECPE 170 Jeff Shafer University of the Pacific Processor Architectures 2 Schedule Exam 3 Tuesday, December 6 th Caches Virtual Memory Input / Output OperaKng Systems Compilers & Assemblers Processor Architecture

More information

Facing the Challenges for Real-Time Software Development on Multi-Cores

Facing the Challenges for Real-Time Software Development on Multi-Cores Facing the Challenges for Real-Time Software Development on Multi-Cores Dr. Fridtjof Siebert aicas GmbH Haid-und-Neu-Str. 18 76131 Karlsruhe, Germany siebert@aicas.com Abstract Multicore systems introduce

More information

Historically, Huge Performance Gains came from Huge Clock Frequency Increases Unfortunately.

Historically, Huge Performance Gains came from Huge Clock Frequency Increases Unfortunately. Historically, Huge Performance Gains came from Huge Clock Frequency Increases Unfortunately. Hardware Solution Evolution of Computer Architectures Micro-Scopic View Clock Rate Limits Have Been Reached

More information

Optimizing Shared Resource Contention in HPC Clusters

Optimizing Shared Resource Contention in HPC Clusters Optimizing Shared Resource Contention in HPC Clusters Sergey Blagodurov Simon Fraser University Alexandra Fedorova Simon Fraser University Abstract Contention for shared resources in HPC clusters occurs

More information

Improve Business Productivity and User Experience with a SanDisk Powered SQL Server 2014 In-Memory OLTP Database

Improve Business Productivity and User Experience with a SanDisk Powered SQL Server 2014 In-Memory OLTP Database WHITE PAPER Improve Business Productivity and User Experience with a SanDisk Powered SQL Server 2014 In-Memory OLTP Database 951 SanDisk Drive, Milpitas, CA 95035 www.sandisk.com Table of Contents Executive

More information

Transactions and ACID in MongoDB

Transactions and ACID in MongoDB Transactions and ACID in MongoDB Kevin Swingler Contents Recap of ACID transactions in RDBMSs Transactions and ACID in MongoDB 1 Concurrency Databases are almost always accessed by multiple users concurrently

More information

Tashkent: Uniting Durability with Transaction Ordering for High-Performance Scalable Database Replication

Tashkent: Uniting Durability with Transaction Ordering for High-Performance Scalable Database Replication Tashkent: Uniting Durability with Transaction Ordering for High-Performance Scalable Database Replication Sameh Elnikety Steven Dropsho Fernando Pedone School of Computer and Communication Sciences EPFL

More information

Chapter 18: Database System Architectures. Centralized Systems

Chapter 18: Database System Architectures. Centralized Systems Chapter 18: Database System Architectures! Centralized Systems! Client--Server Systems! Parallel Systems! Distributed Systems! Network Types 18.1 Centralized Systems! Run on a single computer system and

More information

How To Write A Multi Threaded Software On A Single Core (Or Multi Threaded) System

How To Write A Multi Threaded Software On A Single Core (Or Multi Threaded) System Multicore Systems Challenges for the Real-Time Software Developer Dr. Fridtjof Siebert aicas GmbH Haid-und-Neu-Str. 18 76131 Karlsruhe, Germany siebert@aicas.com Abstract Multicore systems have become

More information

Database Replication with Oracle 11g and MS SQL Server 2008

Database Replication with Oracle 11g and MS SQL Server 2008 Database Replication with Oracle 11g and MS SQL Server 2008 Flavio Bolfing Software and Systems University of Applied Sciences Chur, Switzerland www.hsr.ch/mse Abstract Database replication is used widely

More information

Four Keys to Successful Multicore Optimization for Machine Vision. White Paper

Four Keys to Successful Multicore Optimization for Machine Vision. White Paper Four Keys to Successful Multicore Optimization for Machine Vision White Paper Optimizing a machine vision application for multicore PCs can be a complex process with unpredictable results. Developers need

More information

Chapter 14: Recovery System

Chapter 14: Recovery System Chapter 14: Recovery System Chapter 14: Recovery System Failure Classification Storage Structure Recovery and Atomicity Log-Based Recovery Remote Backup Systems Failure Classification Transaction failure

More information

RevoScaleR Speed and Scalability

RevoScaleR Speed and Scalability EXECUTIVE WHITE PAPER RevoScaleR Speed and Scalability By Lee Edlefsen Ph.D., Chief Scientist, Revolution Analytics Abstract RevoScaleR, the Big Data predictive analytics library included with Revolution

More information

Reliable Systolic Computing through Redundancy

Reliable Systolic Computing through Redundancy Reliable Systolic Computing through Redundancy Kunio Okuda 1, Siang Wun Song 1, and Marcos Tatsuo Yamamoto 1 Universidade de São Paulo, Brazil, {kunio,song,mty}@ime.usp.br, http://www.ime.usp.br/ song/

More information

Scheduling. Scheduling. Scheduling levels. Decision to switch the running process can take place under the following circumstances:

Scheduling. Scheduling. Scheduling levels. Decision to switch the running process can take place under the following circumstances: Scheduling Scheduling Scheduling levels Long-term scheduling. Selects which jobs shall be allowed to enter the system. Only used in batch systems. Medium-term scheduling. Performs swapin-swapout operations

More information

Parallel Programming Survey

Parallel Programming Survey Christian Terboven 02.09.2014 / Aachen, Germany Stand: 26.08.2014 Version 2.3 IT Center der RWTH Aachen University Agenda Overview: Processor Microarchitecture Shared-Memory

More information

Multi-core Programming System Overview

Multi-core Programming System Overview Multi-core Programming System Overview Based on slides from Intel Software College and Multi-Core Programming increasing performance through software multi-threading by Shameem Akhter and Jason Roberts,

More information

Copyright www.agileload.com 1

Copyright www.agileload.com 1 Copyright www.agileload.com 1 INTRODUCTION Performance testing is a complex activity where dozens of factors contribute to its success and effective usage of all those factors is necessary to get the accurate

More information

WITH A FUSION POWERED SQL SERVER 2014 IN-MEMORY OLTP DATABASE

WITH A FUSION POWERED SQL SERVER 2014 IN-MEMORY OLTP DATABASE WITH A FUSION POWERED SQL SERVER 2014 IN-MEMORY OLTP DATABASE 1 W W W. F U S I ON I O.COM Table of Contents Table of Contents... 2 Executive Summary... 3 Introduction: In-Memory Meets iomemory... 4 What

More information

An Easier Way for Cross-Platform Data Acquisition Application Development

An Easier Way for Cross-Platform Data Acquisition Application Development An Easier Way for Cross-Platform Data Acquisition Application Development For industrial automation and measurement system developers, software technology continues making rapid progress. Software engineers

More information

Predictive modeling for software transactional memory

Predictive modeling for software transactional memory VU University Amsterdam BMI Paper Predictive modeling for software transactional memory Author: Tim Stokman Supervisor: Sandjai Bhulai October, Abstract In this paper a new kind of concurrency type named

More information

ATLAS: SOFTWARE DEVELOPMENT ENVIRONMENT FOR HARDWARE TRANSACTIONAL MEMORY

ATLAS: SOFTWARE DEVELOPMENT ENVIRONMENT FOR HARDWARE TRANSACTIONAL MEMORY ATLAS: SOFTWARE DEVELOPMENT ENVIRONMENT FOR HARDWARE TRANSACTIONAL MEMORY A DISSERTATION SUBMITTED TO THE DEPARTMENT OF ELECTRICAL ENGINEERING AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY

More information

MAXIMIZING RESTORABLE THROUGHPUT IN MPLS NETWORKS

MAXIMIZING RESTORABLE THROUGHPUT IN MPLS NETWORKS MAXIMIZING RESTORABLE THROUGHPUT IN MPLS NETWORKS 1 M.LAKSHMI, 2 N.LAKSHMI 1 Assitant Professor, Dept.of.Computer science, MCC college.pattukottai. 2 Research Scholar, Dept.of.Computer science, MCC college.pattukottai.

More information

Choosing a Computer for Running SLX, P3D, and P5

Choosing a Computer for Running SLX, P3D, and P5 Choosing a Computer for Running SLX, P3D, and P5 This paper is based on my experience purchasing a new laptop in January, 2010. I ll lead you through my selection criteria and point you to some on-line

More information

OBJECTIVE ANALYSIS WHITE PAPER MATCH FLASH. TO THE PROCESSOR Why Multithreading Requires Parallelized Flash ATCHING

OBJECTIVE ANALYSIS WHITE PAPER MATCH FLASH. TO THE PROCESSOR Why Multithreading Requires Parallelized Flash ATCHING OBJECTIVE ANALYSIS WHITE PAPER MATCH ATCHING FLASH TO THE PROCESSOR Why Multithreading Requires Parallelized Flash T he computing community is at an important juncture: flash memory is now generally accepted

More information

Application Performance Monitoring: Trade-Off between Overhead Reduction and Maintainability

Application Performance Monitoring: Trade-Off between Overhead Reduction and Maintainability Application Performance Monitoring: Trade-Off between Overhead Reduction and Maintainability Jan Waller, Florian Fittkau, and Wilhelm Hasselbring 2014-11-27 Waller, Fittkau, Hasselbring Application Performance

More information

PIONEER RESEARCH & DEVELOPMENT GROUP

PIONEER RESEARCH & DEVELOPMENT GROUP SURVEY ON RAID Aishwarya Airen 1, Aarsh Pandit 2, Anshul Sogani 3 1,2,3 A.I.T.R, Indore. Abstract RAID stands for Redundant Array of Independent Disk that is a concept which provides an efficient way for

More information

A Comparative Study on Vega-HTTP & Popular Open-source Web-servers

A Comparative Study on Vega-HTTP & Popular Open-source Web-servers A Comparative Study on Vega-HTTP & Popular Open-source Web-servers Happiest People. Happiest Customers Contents Abstract... 3 Introduction... 3 Performance Comparison... 4 Architecture... 5 Diagram...

More information

How To Improve Performance On A Single Chip Computer

How To Improve Performance On A Single Chip Computer : Redundant Arrays of Inexpensive Disks this discussion is based on the paper:» A Case for Redundant Arrays of Inexpensive Disks (),» David A Patterson, Garth Gibson, and Randy H Katz,» In Proceedings

More information

Scaling Objectivity Database Performance with Panasas Scale-Out NAS Storage

Scaling Objectivity Database Performance with Panasas Scale-Out NAS Storage White Paper Scaling Objectivity Database Performance with Panasas Scale-Out NAS Storage A Benchmark Report August 211 Background Objectivity/DB uses a powerful distributed processing architecture to manage

More information

Unit A451: Computer systems and programming. Section 2: Computing Hardware 1/5: Central Processing Unit

Unit A451: Computer systems and programming. Section 2: Computing Hardware 1/5: Central Processing Unit Unit A451: Computer systems and programming Section 2: Computing Hardware 1/5: Central Processing Unit Section Objectives Candidates should be able to: (a) State the purpose of the CPU (b) Understand the

More information

How System Settings Impact PCIe SSD Performance

How System Settings Impact PCIe SSD Performance How System Settings Impact PCIe SSD Performance Suzanne Ferreira R&D Engineer Micron Technology, Inc. July, 2012 As solid state drives (SSDs) continue to gain ground in the enterprise server and storage

More information

Tradeoffs in Transactional Memory Virtualization

Tradeoffs in Transactional Memory Virtualization Tradeoffs in Transactional Memory Virtualization JaeWoong Chung, Chi Cao Minh, Austen McDonald, Travis Skare, Hassan Chafi, Brian D. Carlstrom, Christos Kozyrakis and Kunle Olukotun Computer Systems Laboratory

More information

This paper defines as "Classical"

This paper defines as Classical Principles of Transactional Approach in the Classical Web-based Systems and the Cloud Computing Systems - Comparative Analysis Vanya Lazarova * Summary: This article presents a comparative analysis of

More information

Low Overhead Concurrency Control for Partitioned Main Memory Databases

Low Overhead Concurrency Control for Partitioned Main Memory Databases Low Overhead Concurrency Control for Partitioned Main Memory bases Evan P. C. Jones MIT CSAIL Cambridge, MA, USA evanj@csail.mit.edu Daniel J. Abadi Yale University New Haven, CT, USA dna@cs.yale.edu Samuel

More information

Outline. Failure Types

Outline. Failure Types Outline Database Management and Tuning Johann Gamper Free University of Bozen-Bolzano Faculty of Computer Science IDSE Unit 11 1 2 Conclusion Acknowledgements: The slides are provided by Nikolaus Augsten

More information

Boosting Database Batch workloads using Flash Memory SSDs

Boosting Database Batch workloads using Flash Memory SSDs Boosting Database Batch workloads using Flash Memory SSDs Won-Gill Oh and Sang-Won Lee School of Information and Communication Engineering SungKyunKwan University, 27334 2066, Seobu-Ro, Jangan-Gu, Suwon-Si,

More information

Two-Level Metadata Management for Data Deduplication System

Two-Level Metadata Management for Data Deduplication System Two-Level Metadata Management for Data Deduplication System Jin San Kong 1, Min Ja Kim 2, Wan Yeon Lee 3.,Young Woong Ko 1 1 Dept. of Computer Engineering, Hallym University Chuncheon, Korea { kongjs,

More information

Cluster Computing. ! Fault tolerance. ! Stateless. ! Throughput. ! Stateful. ! Response time. Architectures. Stateless vs. Stateful.

Cluster Computing. ! Fault tolerance. ! Stateless. ! Throughput. ! Stateful. ! Response time. Architectures. Stateless vs. Stateful. Architectures Cluster Computing Job Parallelism Request Parallelism 2 2010 VMware Inc. All rights reserved Replication Stateless vs. Stateful! Fault tolerance High availability despite failures If one

More information

Survey on Comparative Analysis of Database Replication Techniques

Survey on Comparative Analysis of Database Replication Techniques 72 Survey on Comparative Analysis of Database Replication Techniques Suchit Sapate, Student, Computer Science and Engineering, St. Vincent Pallotti College, Nagpur, India Minakshi Ramteke, Student, Computer

More information

A Study on the Scalability of Hybrid LS-DYNA on Multicore Architectures

A Study on the Scalability of Hybrid LS-DYNA on Multicore Architectures 11 th International LS-DYNA Users Conference Computing Technology A Study on the Scalability of Hybrid LS-DYNA on Multicore Architectures Yih-Yih Lin Hewlett-Packard Company Abstract In this paper, the

More information

Intel DPDK Boosts Server Appliance Performance White Paper

Intel DPDK Boosts Server Appliance Performance White Paper Intel DPDK Boosts Server Appliance Performance Intel DPDK Boosts Server Appliance Performance Introduction As network speeds increase to 40G and above, both in the enterprise and data center, the bottlenecks

More information

A Thread Monitoring System for Multithreaded Java Programs

A Thread Monitoring System for Multithreaded Java Programs A Thread Monitoring System for Multithreaded Java Programs Sewon Moon and Byeong-Mo Chang Department of Computer Science Sookmyung Women s University, Seoul 140-742, Korea wonsein@nate.com, chang@sookmyung.ac.kr

More information

COMPUTER HARDWARE. Input- Output and Communication Memory Systems

COMPUTER HARDWARE. Input- Output and Communication Memory Systems COMPUTER HARDWARE Input- Output and Communication Memory Systems Computer I/O I/O devices commonly found in Computer systems Keyboards Displays Printers Magnetic Drives Compact disk read only memory (CD-ROM)

More information

Parallel Algorithm Engineering

Parallel Algorithm Engineering Parallel Algorithm Engineering Kenneth S. Bøgh PhD Fellow Based on slides by Darius Sidlauskas Outline Background Current multicore architectures UMA vs NUMA The openmp framework Examples Software crisis

More information

Centralized Systems. A Centralized Computer System. Chapter 18: Database System Architectures

Centralized Systems. A Centralized Computer System. Chapter 18: Database System Architectures Chapter 18: Database System Architectures Centralized Systems! Centralized Systems! Client--Server Systems! Parallel Systems! Distributed Systems! Network Types! Run on a single computer system and do

More information

How To Build A Cloud Computer

How To Build A Cloud Computer Introducing the Singlechip Cloud Computer Exploring the Future of Many-core Processors White Paper Intel Labs Jim Held Intel Fellow, Intel Labs Director, Tera-scale Computing Research Sean Koehl Technology

More information

File System & Device Drive. Overview of Mass Storage Structure. Moving head Disk Mechanism. HDD Pictures 11/13/2014. CS341: Operating System

File System & Device Drive. Overview of Mass Storage Structure. Moving head Disk Mechanism. HDD Pictures 11/13/2014. CS341: Operating System CS341: Operating System Lect 36: 1 st Nov 2014 Dr. A. Sahu Dept of Comp. Sc. & Engg. Indian Institute of Technology Guwahati File System & Device Drive Mass Storage Disk Structure Disk Arm Scheduling RAID

More information

Real Time Network Server Monitoring using Smartphone with Dynamic Load Balancing

Real Time Network Server Monitoring using Smartphone with Dynamic Load Balancing www.ijcsi.org 227 Real Time Network Server Monitoring using Smartphone with Dynamic Load Balancing Dhuha Basheer Abdullah 1, Zeena Abdulgafar Thanoon 2, 1 Computer Science Department, Mosul University,

More information

Evaluating HDFS I/O Performance on Virtualized Systems

Evaluating HDFS I/O Performance on Virtualized Systems Evaluating HDFS I/O Performance on Virtualized Systems Xin Tang xtang@cs.wisc.edu University of Wisconsin-Madison Department of Computer Sciences Abstract Hadoop as a Service (HaaS) has received increasing

More information

BRINGING INFORMATION RETRIEVAL BACK TO DATABASE MANAGEMENT SYSTEMS

BRINGING INFORMATION RETRIEVAL BACK TO DATABASE MANAGEMENT SYSTEMS BRINGING INFORMATION RETRIEVAL BACK TO DATABASE MANAGEMENT SYSTEMS Khaled Nagi Dept. of Computer and Systems Engineering, Faculty of Engineering, Alexandria University, Egypt. khaled.nagi@eng.alex.edu.eg

More information

1.264 Lecture 15. SQL transactions, security, indexes

1.264 Lecture 15. SQL transactions, security, indexes 1.264 Lecture 15 SQL transactions, security, indexes Download BeefData.csv and Lecture15Download.sql Next class: Read Beginning ASP.NET chapter 1. Exercise due after class (5:00) 1 SQL Server diagrams

More information

Reducing Memory in Software-Based Thread-Level Speculation for JavaScript Virtual Machine Execution of Web Applications

Reducing Memory in Software-Based Thread-Level Speculation for JavaScript Virtual Machine Execution of Web Applications Reducing Memory in Software-Based Thread-Level Speculation for JavaScript Virtual Machine Execution of Web Applications Abstract Thread-Level Speculation has been used to take advantage of multicore processors

More information

Performance Evaluation of Adaptivity in Software Transactional Memory

Performance Evaluation of Adaptivity in Software Transactional Memory Performance Evaluation of Adaptivity in Software Transactional Memory Mathias Payer ETH Zurich, Switzerland mathias.payer@inf.ethz.ch Thomas R. Gross ETH Zurich, Switzerland trg@inf.ethz.ch Abstract Transactional

More information

An Overview of Distributed Databases

An Overview of Distributed Databases International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 4, Number 2 (2014), pp. 207-214 International Research Publications House http://www. irphouse.com /ijict.htm An Overview

More information

MAGENTO HOSTING Progressive Server Performance Improvements

MAGENTO HOSTING Progressive Server Performance Improvements MAGENTO HOSTING Progressive Server Performance Improvements Simple Helix, LLC 4092 Memorial Parkway Ste 202 Huntsville, AL 35802 sales@simplehelix.com 1.866.963.0424 www.simplehelix.com 2 Table of Contents

More information

Thesis Proposal: Improving the Performance of Synchronization in Concurrent Haskell

Thesis Proposal: Improving the Performance of Synchronization in Concurrent Haskell Thesis Proposal: Improving the Performance of Synchronization in Concurrent Haskell Ryan Yates 5-5-2014 1/21 Introduction Outline Thesis Why Haskell? Preliminary work Hybrid TM for GHC Obstacles to Performance

More information

Multi-core processors An overview

Multi-core processors An overview Multi-core processors An overview Balaji Venu 1 1 Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK Abstract Microprocessors have revolutionized the world we

More information

Efficient Parallel Graph Exploration on Multi-Core CPU and GPU

Efficient Parallel Graph Exploration on Multi-Core CPU and GPU Efficient Parallel Graph Exploration on Multi-Core CPU and GPU Pervasive Parallelism Laboratory Stanford University Sungpack Hong, Tayo Oguntebi, and Kunle Olukotun Graph and its Applications Graph Fundamental

More information

Database Tuning and Physical Design: Execution of Transactions

Database Tuning and Physical Design: Execution of Transactions Database Tuning and Physical Design: Execution of Transactions David Toman School of Computer Science University of Waterloo Introduction to Databases CS348 David Toman (University of Waterloo) Transaction

More information

Operating Systems 4 th Class

Operating Systems 4 th Class Operating Systems 4 th Class Lecture 1 Operating Systems Operating systems are essential part of any computer system. Therefore, a course in operating systems is an essential part of any computer science

More information

Hardware Configuration Guide

Hardware Configuration Guide Hardware Configuration Guide Contents Contents... 1 Annotation... 1 Factors to consider... 2 Machine Count... 2 Data Size... 2 Data Size Total... 2 Daily Backup Data Size... 2 Unique Data Percentage...

More information

SQL Server 2014 New Features/In- Memory Store. Juergen Thomas Microsoft Corporation

SQL Server 2014 New Features/In- Memory Store. Juergen Thomas Microsoft Corporation SQL Server 2014 New Features/In- Memory Store Juergen Thomas Microsoft Corporation AGENDA 1. SQL Server 2014 what and when 2. SQL Server 2014 In-Memory 3. SQL Server 2014 in IaaS scenarios 2 SQL Server

More information

Scaling Database Performance in Azure

Scaling Database Performance in Azure Scaling Database Performance in Azure Results of Microsoft-funded Testing Q1 2015 2015 2014 ScaleArc. All Rights Reserved. 1 Test Goals and Background Info Test Goals and Setup Test goals Microsoft commissioned

More information

Sawmill Log Analyzer Best Practices!! Page 1 of 6. Sawmill Log Analyzer Best Practices

Sawmill Log Analyzer Best Practices!! Page 1 of 6. Sawmill Log Analyzer Best Practices Sawmill Log Analyzer Best Practices!! Page 1 of 6 Sawmill Log Analyzer Best Practices! Sawmill Log Analyzer Best Practices!! Page 2 of 6 This document describes best practices for the Sawmill universal

More information

Autodesk Inventor on the Macintosh

Autodesk Inventor on the Macintosh Autodesk Inventor on the Macintosh FREQUENTLY ASKED QUESTIONS 1. Can I install Autodesk Inventor on a Mac? 2. What is Boot Camp? 3. What is Parallels? 4. How does Boot Camp differ from Virtualization?

More information

Hadoop Scheduler w i t h Deadline Constraint

Hadoop Scheduler w i t h Deadline Constraint Hadoop Scheduler w i t h Deadline Constraint Geetha J 1, N UdayBhaskar 2, P ChennaReddy 3,Neha Sniha 4 1,4 Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore,

More information

PHYSICAL CORES V. ENHANCED THREADING SOFTWARE: PERFORMANCE EVALUATION WHITEPAPER

PHYSICAL CORES V. ENHANCED THREADING SOFTWARE: PERFORMANCE EVALUATION WHITEPAPER PHYSICAL CORES V. ENHANCED THREADING SOFTWARE: PERFORMANCE EVALUATION WHITEPAPER Preface Today s world is ripe with computing technology. Computing technology is all around us and it s often difficult

More information

Improving In-Memory Database Index Performance with Intel R Transactional Synchronization Extensions

Improving In-Memory Database Index Performance with Intel R Transactional Synchronization Extensions Appears in the 20th International Symposium On High-Performance Computer Architecture, Feb. 15 - Feb. 19, 2014. Improving In-Memory Database Index Performance with Intel R Transactional Synchronization

More information

I N T E R S Y S T E M S W H I T E P A P E R INTERSYSTEMS CACHÉ AS AN ALTERNATIVE TO IN-MEMORY DATABASES. David Kaaret InterSystems Corporation

I N T E R S Y S T E M S W H I T E P A P E R INTERSYSTEMS CACHÉ AS AN ALTERNATIVE TO IN-MEMORY DATABASES. David Kaaret InterSystems Corporation INTERSYSTEMS CACHÉ AS AN ALTERNATIVE TO IN-MEMORY DATABASES David Kaaret InterSystems Corporation INTERSYSTEMS CACHÉ AS AN ALTERNATIVE TO IN-MEMORY DATABASES Introduction To overcome the performance limitations

More information