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  • IBM enrichit sa solution de Business Intelligence Cognos Express avec Planner un nouvel outil de planification pour les PME

    IBM enrichit sa solution de Business Intelligence Cognos Express Avec Planner un nouvel outil de planification pour les PME IBM vient de lancer « Planner », un nouveau module pour sa solution d'analyse et d'informatique décisionnelle « Cognos Express ». Le module est spécialement conçu pour répondre aux besoins des moyennes entreprises. IBM Cognos Express Planner devrait offrir une démarche structurée de planification, facile à déployer et à utiliser qui permet aux utilisateurs de réagir rapidement aux conditions changeantes du marché. D'après IBM, l'interface utilisateur de Planner, simplifiée, devrait permettre aux financiers et aux non-financiers de collaborer en...

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  • SQLAuthority News Storage and SQL Server Capacity Planning and configuration SharePoint Server 201

    Just a day ago, I was asked how do you plan SQL Server Storage Capacity. Here is the excellent article published by Microsoft regarding SQL Server capacity planning for SharePoint 2010. This article touches all the vital areas of this subject. Here are the bullet points for the same. Gather storage and SQL Server space [...]...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Quantcast Media Planner

    Have you ever wondered what type and how many people are visiting your website? If you answered "yes" to the previous question, you will be pleased to know that there are online tools available that allow people to analyse the relevance and effectiveness of their web pages.

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  • PTLQueue : a scalable bounded-capacity MPMC queue

    - by Dave
    Title: Fast concurrent MPMC queue -- I've used the following concurrent queue algorithm enough that it warrants a blog entry. I'll sketch out the design of a fast and scalable multiple-producer multiple-consumer (MPSC) concurrent queue called PTLQueue. The queue has bounded capacity and is implemented via a circular array. Bounded capacity can be a useful property if there's a mismatch between producer rates and consumer rates where an unbounded queue might otherwise result in excessive memory consumption by virtue of the container nodes that -- in some queue implementations -- are used to hold values. A bounded-capacity queue can provide flow control between components. Beware, however, that bounded collections can also result in resource deadlock if abused. The put() and take() operators are partial and wait for the collection to become non-full or non-empty, respectively. Put() and take() do not allocate memory, and are not vulnerable to the ABA pathologies. The PTLQueue algorithm can be implemented equally well in C/C++ and Java. Partial operators are often more convenient than total methods. In many use cases if the preconditions aren't met, there's nothing else useful the thread can do, so it may as well wait via a partial method. An exception is in the case of work-stealing queues where a thief might scan a set of queues from which it could potentially steal. Total methods return ASAP with a success-failure indication. (It's tempting to describe a queue or API as blocking or non-blocking instead of partial or total, but non-blocking is already an overloaded concurrency term. Perhaps waiting/non-waiting or patient/impatient might be better terms). It's also trivial to construct partial operators by busy-waiting via total operators, but such constructs may be less efficient than an operator explicitly and intentionally designed to wait. A PTLQueue instance contains an array of slots, where each slot has volatile Turn and MailBox fields. The array has power-of-two length allowing mod/div operations to be replaced by masking. We assume sensible padding and alignment to reduce the impact of false sharing. (On x86 I recommend 128-byte alignment and padding because of the adjacent-sector prefetch facility). Each queue also has PutCursor and TakeCursor cursor variables, each of which should be sequestered as the sole occupant of a cache line or sector. You can opt to use 64-bit integers if concerned about wrap-around aliasing in the cursor variables. Put(null) is considered illegal, but the caller or implementation can easily check for and convert null to a distinguished non-null proxy value if null happens to be a value you'd like to pass. Take() will accordingly convert the proxy value back to null. An advantage of PTLQueue is that you can use atomic fetch-and-increment for the partial methods. We initialize each slot at index I with (Turn=I, MailBox=null). Both cursors are initially 0. All shared variables are considered "volatile" and atomics such as CAS and AtomicFetchAndIncrement are presumed to have bidirectional fence semantics. Finally T is the templated type. I've sketched out a total tryTake() method below that allows the caller to poll the queue. tryPut() has an analogous construction. Zebra stripping : alternating row colors for nice-looking code listings. See also google code "prettify" : https://code.google.com/p/google-code-prettify/ Prettify is a javascript module that yields the HTML/CSS/JS equivalent of pretty-print. -- pre:nth-child(odd) { background-color:#ff0000; } pre:nth-child(even) { background-color:#0000ff; } border-left: 11px solid #ccc; margin: 1.7em 0 1.7em 0.3em; background-color:#BFB; font-size:12px; line-height:65%; " // PTLQueue : Put(v) : // producer : partial method - waits as necessary assert v != null assert Mask = 1 && (Mask & (Mask+1)) == 0 // Document invariants // doorway step // Obtain a sequence number -- ticket // As a practical concern the ticket value is temporally unique // The ticket also identifies and selects a slot auto tkt = AtomicFetchIncrement (&PutCursor, 1) slot * s = &Slots[tkt & Mask] // waiting phase : // wait for slot's generation to match the tkt value assigned to this put() invocation. // The "generation" is implicitly encoded as the upper bits in the cursor // above those used to specify the index : tkt div (Mask+1) // The generation serves as an epoch number to identify a cohort of threads // accessing disjoint slots while s-Turn != tkt : Pause assert s-MailBox == null s-MailBox = v // deposit and pass message Take() : // consumer : partial method - waits as necessary auto tkt = AtomicFetchIncrement (&TakeCursor,1) slot * s = &Slots[tkt & Mask] // 2-stage waiting : // First wait for turn for our generation // Acquire exclusive "take" access to slot's MailBox field // Then wait for the slot to become occupied while s-Turn != tkt : Pause // Concurrency in this section of code is now reduced to just 1 producer thread // vs 1 consumer thread. // For a given queue and slot, there will be most one Take() operation running // in this section. // Consumer waits for producer to arrive and make slot non-empty // Extract message; clear mailbox; advance Turn indicator // We have an obvious happens-before relation : // Put(m) happens-before corresponding Take() that returns that same "m" for T v = s-MailBox if v != null : s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 // unlock slot to admit next producer and consumer return v Pause tryTake() : // total method - returns ASAP with failure indication for auto tkt = TakeCursor slot * s = &Slots[tkt & Mask] if s-Turn != tkt : return null T v = s-MailBox // presumptive return value if v == null : return null // ratify tkt and v values and commit by advancing cursor if CAS (&TakeCursor, tkt, tkt+1) != tkt : continue s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 return v The basic idea derives from the Partitioned Ticket Lock "PTL" (US20120240126-A1) and the MultiLane Concurrent Bag (US8689237). The latter is essentially a circular ring-buffer where the elements themselves are queues or concurrent collections. You can think of the PTLQueue as a partitioned ticket lock "PTL" augmented to pass values from lock to unlock via the slots. Alternatively, you could conceptualize of PTLQueue as a degenerate MultiLane bag where each slot or "lane" consists of a simple single-word MailBox instead of a general queue. Each lane in PTLQueue also has a private Turn field which acts like the Turn (Grant) variables found in PTL. Turn enforces strict FIFO ordering and restricts concurrency on the slot mailbox field to at most one simultaneous put() and take() operation. PTL uses a single "ticket" variable and per-slot Turn (grant) fields while MultiLane has distinct PutCursor and TakeCursor cursors and abstract per-slot sub-queues. Both PTL and MultiLane advance their cursor and ticket variables with atomic fetch-and-increment. PTLQueue borrows from both PTL and MultiLane and has distinct put and take cursors and per-slot Turn fields. Instead of a per-slot queues, PTLQueue uses a simple single-word MailBox field. PutCursor and TakeCursor act like a pair of ticket locks, conferring "put" and "take" access to a given slot. PutCursor, for instance, assigns an incoming put() request to a slot and serves as a PTL "Ticket" to acquire "put" permission to that slot's MailBox field. To better explain the operation of PTLQueue we deconstruct the operation of put() and take() as follows. Put() first increments PutCursor obtaining a new unique ticket. That ticket value also identifies a slot. Put() next waits for that slot's Turn field to match that ticket value. This is tantamount to using a PTL to acquire "put" permission on the slot's MailBox field. Finally, having obtained exclusive "put" permission on the slot, put() stores the message value into the slot's MailBox. Take() similarly advances TakeCursor, identifying a slot, and then acquires and secures "take" permission on a slot by waiting for Turn. Take() then waits for the slot's MailBox to become non-empty, extracts the message, and clears MailBox. Finally, take() advances the slot's Turn field, which releases both "put" and "take" access to the slot's MailBox. Note the asymmetry : put() acquires "put" access to the slot, but take() releases that lock. At any given time, for a given slot in a PTLQueue, at most one thread has "put" access and at most one thread has "take" access. This restricts concurrency from general MPMC to 1-vs-1. We have 2 ticket locks -- one for put() and one for take() -- each with its own "ticket" variable in the form of the corresponding cursor, but they share a single "Grant" egress variable in the form of the slot's Turn variable. Advancing the PutCursor, for instance, serves two purposes. First, we obtain a unique ticket which identifies a slot. Second, incrementing the cursor is the doorway protocol step to acquire the per-slot mutual exclusion "put" lock. The cursors and operations to increment those cursors serve double-duty : slot-selection and ticket assignment for locking the slot's MailBox field. At any given time a slot MailBox field can be in one of the following states: empty with no pending operations -- neutral state; empty with one or more waiting take() operations pending -- deficit; occupied with no pending operations; occupied with one or more waiting put() operations -- surplus; empty with a pending put() or pending put() and take() operations -- transitional; or occupied with a pending take() or pending put() and take() operations -- transitional. The partial put() and take() operators can be implemented with an atomic fetch-and-increment operation, which may confer a performance advantage over a CAS-based loop. In addition we have independent PutCursor and TakeCursor cursors. Critically, a put() operation modifies PutCursor but does not access the TakeCursor and a take() operation modifies the TakeCursor cursor but does not access the PutCursor. This acts to reduce coherence traffic relative to some other queue designs. It's worth noting that slow threads or obstruction in one slot (or "lane") does not impede or obstruct operations in other slots -- this gives us some degree of obstruction isolation. PTLQueue is not lock-free, however. The implementation above is expressed with polite busy-waiting (Pause) but it's trivial to implement per-slot parking and unparking to deschedule waiting threads. It's also easy to convert the queue to a more general deque by replacing the PutCursor and TakeCursor cursors with Left/Front and Right/Back cursors that can move either direction. Specifically, to push and pop from the "left" side of the deque we would decrement and increment the Left cursor, respectively, and to push and pop from the "right" side of the deque we would increment and decrement the Right cursor, respectively. We used a variation of PTLQueue for message passing in our recent OPODIS 2013 paper. ul { list-style:none; padding-left:0; padding:0; margin:0; margin-left:0; } ul#myTagID { padding: 0px; margin: 0px; list-style:none; margin-left:0;} -- -- There's quite a bit of related literature in this area. I'll call out a few relevant references: Wilson's NYU Courant Institute UltraComputer dissertation from 1988 is classic and the canonical starting point : Operating System Data Structures for Shared-Memory MIMD Machines with Fetch-and-Add. Regarding provenance and priority, I think PTLQueue or queues effectively equivalent to PTLQueue have been independently rediscovered a number of times. See CB-Queue and BNPBV, below, for instance. But Wilson's dissertation anticipates the basic idea and seems to predate all the others. Gottlieb et al : Basic Techniques for the Efficient Coordination of Very Large Numbers of Cooperating Sequential Processors Orozco et al : CB-Queue in Toward high-throughput algorithms on many-core architectures which appeared in TACO 2012. Meneghin et al : BNPVB family in Performance evaluation of inter-thread communication mechanisms on multicore/multithreaded architecture Dmitry Vyukov : bounded MPMC queue (highly recommended) Alex Otenko : US8607249 (highly related). John Mellor-Crummey : Concurrent queues: Practical fetch-and-phi algorithms. Technical Report 229, Department of Computer Science, University of Rochester Thomasson : FIFO Distributed Bakery Algorithm (very similar to PTLQueue). Scott and Scherer : Dual Data Structures I'll propose an optimization left as an exercise for the reader. Say we wanted to reduce memory usage by eliminating inter-slot padding. Such padding is usually "dark" memory and otherwise unused and wasted. But eliminating the padding leaves us at risk of increased false sharing. Furthermore lets say it was usually the case that the PutCursor and TakeCursor were numerically close to each other. (That's true in some use cases). We might still reduce false sharing by incrementing the cursors by some value other than 1 that is not trivially small and is coprime with the number of slots. Alternatively, we might increment the cursor by one and mask as usual, resulting in a logical index. We then use that logical index value to index into a permutation table, yielding an effective index for use in the slot array. The permutation table would be constructed so that nearby logical indices would map to more distant effective indices. (Open question: what should that permutation look like? Possibly some perversion of a Gray code or De Bruijn sequence might be suitable). As an aside, say we need to busy-wait for some condition as follows : "while C == 0 : Pause". Lets say that C is usually non-zero, so we typically don't wait. But when C happens to be 0 we'll have to spin for some period, possibly brief. We can arrange for the code to be more machine-friendly with respect to the branch predictors by transforming the loop into : "if C == 0 : for { Pause; if C != 0 : break; }". Critically, we want to restructure the loop so there's one branch that controls entry and another that controls loop exit. A concern is that your compiler or JIT might be clever enough to transform this back to "while C == 0 : Pause". You can sometimes avoid this by inserting a call to a some type of very cheap "opaque" method that the compiler can't elide or reorder. On Solaris, for instance, you could use :"if C == 0 : { gethrtime(); for { Pause; if C != 0 : break; }}". It's worth noting the obvious duality between locks and queues. If you have strict FIFO lock implementation with local spinning and succession by direct handoff such as MCS or CLH,then you can usually transform that lock into a queue. Hidden commentary and annotations - invisible : * And of course there's a well-known duality between queues and locks, but I'll leave that topic for another blog post. * Compare and contrast : PTLQ vs PTL and MultiLane * Equivalent : Turn; seq; sequence; pos; position; ticket * Put = Lock; Deposit Take = identify and reserve slot; wait; extract & clear; unlock * conceptualize : Distinct PutLock and TakeLock implemented as ticket lock or PTL Distinct arrival cursors but share per-slot "Turn" variable provides exclusive role-based access to slot's mailbox field put() acquires exclusive access to a slot for purposes of "deposit" assigns slot round-robin and then acquires deposit access rights/perms to that slot take() acquires exclusive access to slot for purposes of "withdrawal" assigns slot round-robin and then acquires withdrawal access rights/perms to that slot At any given time, only one thread can have withdrawal access to a slot at any given time, only one thread can have deposit access to a slot Permissible for T1 to have deposit access and T2 to simultaneously have withdrawal access * round-robin for the purposes of; role-based; access mode; access role mailslot; mailbox; allocate/assign/identify slot rights; permission; license; access permission; * PTL/Ticket hybrid Asymmetric usage ; owner oblivious lock-unlock pairing K-exclusion add Grant cursor pass message m from lock to unlock via Slots[] array Cursor performs 2 functions : + PTL ticket + Assigns request to slot in round-robin fashion Deconstruct protocol : explication put() : allocate slot in round-robin fashion acquire PTL for "put" access store message into slot associated with PTL index take() : Acquire PTL for "take" access // doorway step seq = fetchAdd (&Grant, 1) s = &Slots[seq & Mask] // waiting phase while s-Turn != seq : pause Extract : wait for s-mailbox to be full v = s-mailbox s-mailbox = null Release PTL for both "put" and "take" access s-Turn = seq + Mask + 1 * Slot round-robin assignment and lock "doorway" protocol leverage the same cursor and FetchAdd operation on that cursor FetchAdd (&Cursor,1) + round-robin slot assignment and dispersal + PTL/ticket lock "doorway" step waiting phase is via "Turn" field in slot * PTLQueue uses 2 cursors -- put and take. Acquire "put" access to slot via PTL-like lock Acquire "take" access to slot via PTL-like lock 2 locks : put and take -- at most one thread can access slot's mailbox Both locks use same "turn" field Like multilane : 2 cursors : put and take slot is simple 1-capacity mailbox instead of queue Borrow per-slot turn/grant from PTL Provides strict FIFO Lock slot : put-vs-put take-vs-take at most one put accesses slot at any one time at most one put accesses take at any one time reduction to 1-vs-1 instead of N-vs-M concurrency Per slot locks for put/take Release put/take by advancing turn * is instrumental in ... * P-V Semaphore vs lock vs K-exclusion * See also : FastQueues-excerpt.java dice-etc/queue-mpmc-bounded-blocking-circular-xadd/ * PTLQueue is the same as PTLQB - identical * Expedient return; ASAP; prompt; immediately * Lamport's Bakery algorithm : doorway step then waiting phase Threads arriving at doorway obtain a unique ticket number Threads enter in ticket order * In the terminology of Reed and Kanodia a ticket lock corresponds to the busy-wait implementation of a semaphore using an eventcount and a sequencer It can also be thought of as an optimization of Lamport's bakery lock was designed for fault-tolerance rather than performance Instead of spinning on the release counter, processors using a bakery lock repeatedly examine the tickets of their peers --

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  • New version: Sun Rack II capacity calculator

    - by uwes
    A new release of the Sun Rack II capacity calculator is available on eSTEP portal. The tool calculates all the data necessary (power requirements, BTU, number of rack units, needed power outlets etc.) while inserting the many different kind of HW equipment in aSun Rack II cabinet (version 1000 and 1200). It takes into consideration most of the available servers, storage devices, tapes, and Netra products. There are also a couple of third party products which are taken into account. The spreadsheet can be downloaded from eSTEP portal. URL: http://launch.oracle.com/ PIN: eSTEP_2011 The material can be found under tab eSTEP Download.

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  • Release 51 of Sun Rack II capacity calculator available

    - by uwes
    A new release of the Sun Rack II capacity calculator is available on eSTEP portal. Just uploaded release 51 of the calculator. The following changes have been integrated: Added LOD date of 30 NOV 2014 for ST25xx M2 (NEP LOD – other customers LOD is 31 MAY 2014) Moved 7420 to EOL HW due to met LOD Bug correction : X4-2 and X4-2L weren’t working. Bug correction : ES1-24 RU are now correctly shown (2 ES1-24 only takes 1 RU) The tool calculates all the data necessary (power requirements, BTU, number of rack units, needed power outlets etc.) while inserting the many different kind of HW equipment in aSun Rack II cabinet (version 1000 and 1200). It takes into consideration most of the available servers, storage devices, tapes, and Netra products. There are also a couple of third party products which are taken into account. The spreadsheet can be downloaded from eSTEP portal. URL: http://launch.oracle.com/ PIN: eSTEP_2011

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  • Is bigger capacity ram faster then smaller capacity ram for same clock and CL?

    - by didibus
    I know that bigger capacity hard-drives with the same RPM are faster then smaller capacity hard-drives. I was wondering if the same is true for ram. Given two ram clocked at 1600mhz and with identical CLs: 9-9-9-24. Is a 2x8 going to perform better then a 2x4 ? Note that I am not asking if having more ram will improve the performance of my PC, I'm asking if the bigger capacity ram performs better. Thank You.

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  • Is bigger capacity ram faster then smaller capacity ram for same clock and CL? [migrated]

    - by didibus
    I know that bigger capacity hard-drives with the same RPM are faster then smaller capacity hard-drives. I was wondering if the same is true for ram. Given two ram clocked at 1600mhz and with identical CLs: 9-9-9-24. Is a 2x8 going to perform better then a 2x4 ? Note that I am not asking if having more ram will improve the performance of my PC, I'm asking if the bigger capacity ram performs better. Thank You.

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  • OBIEE Capacity Planning

    - by THE
    I can not even recall how many times I was asked by a customer what size the machine should be bought to run our Software. Unfortunately Tech Support is not even the right address to answer that question, as a purchase decision is closely tied to the answer. Hence, Tech Support has been limited to the answer: "The biggest machine you can afford" . Many Customers were unhappy with that and have tried to get us to be more precise and that causes a lot of explanation and lengthy discussion. In the end no one is wiser or happier.  Therefore I am happy to report that at least for OBIEE the decision has just been made a whole lot easier. Have a look at the note Oracle BI EE 11g Architectural Deployment: Capacity Planning (Doc ID 1323646.1) The document attached to that note gives you a good overview for teh sizing of the machines that Oracle recommends to run OBIEE (be it a small installation or a bigger distributed installation) If you have any more questions about this topic and what machines we recommend, then get in contact with  Oracle Consulting or speak to your sales representative.

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  • Best things-to-do planner software

    - by ORA600
    Can anyone recommend a good things-to-do planner software with the following features: - tags attached to planned record, ability to filter by them - Outlook-style calendar - Both Windows and Linux - Preferably free Thank you.

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  • How the SPARC T4 Processor Optimizes Throughput Capacity: A Case Study

    - by Ruud
    This white paper demonstrates the architected latency hiding features of Oracle’s UltraSPARC T2+ and SPARC T4 processors That is the first sentence from this technical white paper, but what does it exactly mean? Let's consider a very simple example, the computation of a = b + c. This boils down to the following (pseudo-assembler) instructions that need to be executed: load @b, r1 load @c, r2 add r1,r2,r3 store r3, @a The first two instructions load variables b and c from an address in memory (here symbolized by @b and @c respectively). These values go into registers r1 and r2. The third instruction adds the values in r1 and r2. The result goes into register r3. The fourth instruction stores the contents of r3 into the memory address symbolized by @a. If we're lucky, both b and c are in a nearby cache and the load instructions only take a few processor cycles to execute. That is the good case, but what if b or c, or both, have to come from very far away? Perhaps both of them are in the main memory and then it easily takes hundreds of cycles for the values to arrive in the registers. Meanwhile the processor is doing nothing and simply waits for the data to arrive. Actually, it does something. It burns cycles while waiting. That is a waste of time and energy. Why not use these cycles to execute instructions from another application or thread in case of a parallel program? That is exactly what latency hiding on the SPARC T-Series processors does. It is a hardware feature totally transparent to the user and application. As soon as there is a delay in the execution, the hardware uses these otherwise idle cycles to execute instructions from another process. As a result, the throughput capacity of the system improves because idle cycles are no longer wasted and therefore more jobs can be run per unit of time. This feature has been in the SPARC T-series from the beginning, so why this paper? The difference with previous publications on this topic is in the amount of detail given. How this all works under the hood is fully explained using two example programs. Starting from the assembly language instructions, it is demonstrated in what way these programs execute. To really see what is happening we go down to the processor pipeline level, where the gaps in the execution are, and show in what way these idle cycles are filled by other copies of the same program running simultaneously. Both the SPARC T4 as well as the older UltraSPARC T2+ processor are covered. You may wonder why the UltraSPARC T2+ is included. The focus of this work is on the SPARC T4 processor, but to explain the basic concept of latency hiding at this very low level, we start with the UltraSPARC T2+ processor because it is architecturally a much simpler design. From the single issue, in-order pipelines of this processor we then shift gears and cover how this all works on the much more advanced dual issue, out-of-order architecture of the T4. The analysis and performance experiments have been conducted on both processors. The results depend on the processor, but in all cases the theoretical estimates are confirmed by the experiments. If you're interested to read a lot more about this and find out how things really work under the hood, you can download a copy of the paper here. A paper like this could not have been produced without the help of several other people. I want to thank the co-author of this paper, Jared Smolens, for his very valuable contributions and our highly inspiring discussions. I'm also indebted to Thomas Nau (Ulm University, Germany), Shane Sigler and Mark Woodyard (both at Oracle) for their feedback on earlier versions of this paper. Karen Perkins (Perkins Technical Writing and Editing) and Rick Ramsey at Oracle were very helpful in providing editorial and publishing assistance.

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  • Seeking free project planner with auto resource levelling

    - by mawg
    As the title says, I am seeking a project planner with automatic resource levelling. It must be free for commercial use and a bonus, but not requirement, would be MS project import/export. I like the look of Task Juggler, but it is at a stage of development hovering between v2 and v3. Anything else? Basically, I want to play "what if games" - I will determine the tasks and their effort, and the dependencies between them and then try to figure out how many staff I need. Since many of the tasks can be done in parallel, it is difficult to guess how many are needed. A PM tool with automatic resource levelling seems like a good way to find out.

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  • Standalone server setup for compute capacity

    - by mikera
    I'm developing an application for my company that will require a lot of compute capacity (running some very big mathematical calculations), and looking for some form of server setup to do this. For various reasons, we want to run this on-site in our office rather than hosting it externally. It's been a while since I last had to set up my own servers so I thought I would tap into the collective wisdom of serverfault! My broad requirements are: Budget $30-50k, with an aim to get as much compute capacity as possible for that budget 64-bit servers suitable to run Ubuntu Linux + Java Some relatively standalone rack that can be installed in secure office space Fast/low latency network connections between the servers, but don't really care about connectivity to the outside world Storage capacity shared between the servers - they don't necessarily need their own storage providing they can be booted from a common image Downtime can be tolerated (since the calculations are run in batch mode) The software itself is fault-tolerant, so there is no need for extra resiliency in the server setup (cheap replaceable commodity parts will be fine in general) Given these requirements what kind of setup would you recommend and why?

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  • T-SQL Tuesday - IO capacity planning

    - by Michael Zilberstein
    This post is my contribution to Adam Machanic's T-SQL Tuesday #004 , hosted this time by Mike Walsh . Being applicative DBA, I usually don't take part in discussions which storage to buy or how to configure it. My interaction with IO is usually via PerfMon. When somebody calls me asking why everything is suddenly so slow on database server, "disk queue length" or "average seconds per transfer" counters provide an overwhelming answer in 60-70% of such cases. Sometimes it can be...(read more)

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  • Need help with gimp 2.8 (cpu not used to full capacity) [closed]

    - by Birgir Freyr
    I know this isn't the right place to ask this question but maybe some one here can point me out to were I should place this question (or help me fix it :)). Since I updated Gimp to 2.8 (and let me start by saying how happy I am with the new gimp) I have notice that Gimp only uses 35% max of my CPU power. I have tried changing settings, assigning only one CPU to Gimp (both in gimp preference and in windblows task manager). No matter what settings I choose it only uses 35% of the cpu. If I assign just one Core to it then Gimp will use 100% of that core (which is about 35% of a three core processor I have. Any thoughts? I am using Windblows 7 64 bit, gimp 2.8.0, AMD a6-3500 cpu. I also use Ubuntu (am going to see if it works the same there). Any help would be great.

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  • optimizing a windows server 2003 storage capacity

    - by Hosni
    I have got a windows server 2003 with partitioned Hard drive 10Go and 80Go, and i want to improve the storage capacity as the little partition 10Go is almost full. So i have got choice between partition the hard drive to equal parts, or set up a new hard drive with better storage capacity.knowing that the server has to be on service as soon as possible. Which one may be the better solution that takes less time and less risks?

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  • MacBook Pro battery capacity 65K mAh

    - by Alexander Gladysh
    I have a 15" MacBook Pro 3.1 (that is Late 2007 model AFAIR). I've bought it new a couple of years ago. Recently its on-battery power lifespan became very short (30 to 10 minutes). When my notebook turns itself off due to "low battery" and I press the small button on the battery itself, all LED lights are alight, indicating full charge. When I plug in the power adapter, my Mac displays that "battery is fully charged, finishing charging process" (I have a Russian OS X 10.5.7, so that is a rough translation), but the LEDs on battery itself display (seemingly accurate) status that there are one or two "LEDs still not charged". My battery have as few as 37 recharge cycles (yes, I've neglected calibration over the time I've used it). Battery info programs like iBatt2 report battery capacity of 65 337 mAh (with by-design capacity of 5600 mAh). I get it that something went wrong with battery electronics. I've tried resetting my Mac's PRAM and SMC, it did not changed anything. Now I'm trying to recalibrate the battery, but looks like it does not help as well. Will try to recalibrate it several times in a row. I'd buy a new battery if I knew if it is battery fault, not a notebook's. Any suggestions? Update: After recalibration, my battery status now displays battery capacity of 1500 mAh. But with every recalibration (or simply when I use notebook without power adapter plugged in) this number changes in the range from 200 mAh to 1700 mAh. LEDs on battery now are synchronous with what nodebook thinks on the charge level. Also I've noticed that cycle count changes rather slowly. It is now 39, it was 37 when I've started recalibration, and I went through the process at least ten times... So, the main question is: does it look like that replacing the battery would help me (or does it look like this is notebook's problem)? I guess I should try replacing the battery.

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  • Strange: Planner takes decision with lower cost, but (very) query long runtime

    - by S38
    Facts: PGSQL 8.4.2, Linux I make use of table inheritance Each Table contains 3 million rows Indexes on joining columns are set Table statistics (analyze, vacuum analyze) are up-to-date Only used table is "node" with varios partitioned sub-tables Recursive query (pg = 8.4) Now here is the explained query: WITH RECURSIVE rows AS ( SELECT * FROM ( SELECT r.id, r.set, r.parent, r.masterid FROM d_storage.node_dataset r WHERE masterid = 3533933 ) q UNION ALL SELECT * FROM ( SELECT c.id, c.set, c.parent, r.masterid FROM rows r JOIN a_storage.node c ON c.parent = r.id ) q ) SELECT r.masterid, r.id AS nodeid FROM rows r QUERY PLAN ----------------------------------------------------------------------------------------------------------------------------------------------------------------- CTE Scan on rows r (cost=2742105.92..2862119.94 rows=6000701 width=16) (actual time=0.033..172111.204 rows=4 loops=1) CTE rows -> Recursive Union (cost=0.00..2742105.92 rows=6000701 width=28) (actual time=0.029..172111.183 rows=4 loops=1) -> Index Scan using node_dataset_masterid on node_dataset r (cost=0.00..8.60 rows=1 width=28) (actual time=0.025..0.027 rows=1 loops=1) Index Cond: (masterid = 3533933) -> Hash Join (cost=0.33..262208.33 rows=600070 width=28) (actual time=40628.371..57370.361 rows=1 loops=3) Hash Cond: (c.parent = r.id) -> Append (cost=0.00..211202.04 rows=12001404 width=20) (actual time=0.011..46365.669 rows=12000004 loops=3) -> Seq Scan on node c (cost=0.00..24.00 rows=1400 width=20) (actual time=0.002..0.002 rows=0 loops=3) -> Seq Scan on node_dataset c (cost=0.00..55001.01 rows=3000001 width=20) (actual time=0.007..3426.593 rows=3000001 loops=3) -> Seq Scan on node_stammdaten c (cost=0.00..52059.01 rows=3000001 width=20) (actual time=0.008..9049.189 rows=3000001 loops=3) -> Seq Scan on node_stammdaten_adresse c (cost=0.00..52059.01 rows=3000001 width=20) (actual time=3.455..8381.725 rows=3000001 loops=3) -> Seq Scan on node_testdaten c (cost=0.00..52059.01 rows=3000001 width=20) (actual time=1.810..5259.178 rows=3000001 loops=3) -> Hash (cost=0.20..0.20 rows=10 width=16) (actual time=0.010..0.010 rows=1 loops=3) -> WorkTable Scan on rows r (cost=0.00..0.20 rows=10 width=16) (actual time=0.002..0.004 rows=1 loops=3) Total runtime: 172111.371 ms (16 rows) (END) So far so bad, the planner decides to choose hash joins (good) but no indexes (bad). Now after doing the following: SET enable_hashjoins TO false; The explained query looks like that: QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- CTE Scan on rows r (cost=15198247.00..15318261.02 rows=6000701 width=16) (actual time=0.038..49.221 rows=4 loops=1) CTE rows -> Recursive Union (cost=0.00..15198247.00 rows=6000701 width=28) (actual time=0.032..49.201 rows=4 loops=1) -> Index Scan using node_dataset_masterid on node_dataset r (cost=0.00..8.60 rows=1 width=28) (actual time=0.028..0.031 rows=1 loops=1) Index Cond: (masterid = 3533933) -> Nested Loop (cost=0.00..1507822.44 rows=600070 width=28) (actual time=10.384..16.382 rows=1 loops=3) Join Filter: (r.id = c.parent) -> WorkTable Scan on rows r (cost=0.00..0.20 rows=10 width=16) (actual time=0.001..0.003 rows=1 loops=3) -> Append (cost=0.00..113264.67 rows=3001404 width=20) (actual time=8.546..12.268 rows=1 loops=4) -> Seq Scan on node c (cost=0.00..24.00 rows=1400 width=20) (actual time=0.001..0.001 rows=0 loops=4) -> Bitmap Heap Scan on node_dataset c (cost=58213.87..113214.88 rows=3000001 width=20) (actual time=1.906..1.906 rows=0 loops=4) Recheck Cond: (c.parent = r.id) -> Bitmap Index Scan on node_dataset_parent (cost=0.00..57463.87 rows=3000001 width=0) (actual time=1.903..1.903 rows=0 loops=4) Index Cond: (c.parent = r.id) -> Index Scan using node_stammdaten_parent on node_stammdaten c (cost=0.00..8.60 rows=1 width=20) (actual time=3.272..3.273 rows=0 loops=4) Index Cond: (c.parent = r.id) -> Index Scan using node_stammdaten_adresse_parent on node_stammdaten_adresse c (cost=0.00..8.60 rows=1 width=20) (actual time=4.333..4.333 rows=0 loops=4) Index Cond: (c.parent = r.id) -> Index Scan using node_testdaten_parent on node_testdaten c (cost=0.00..8.60 rows=1 width=20) (actual time=2.745..2.746 rows=0 loops=4) Index Cond: (c.parent = r.id) Total runtime: 49.349 ms (21 rows) (END) - incredibly faster, because indexes were used. Notice: Cost of the second query ist somewhat higher than for the first query. So the main question is: Why does the planner make the first decision, instead of the second? Also interesing: Via SET enable_seqscan TO false; i temp. disabled seq scans. Than the planner used indexes and hash joins, and the query still was slow. So the problem seems to be the hash join. Maybe someone can help in this confusing situation? thx, R.

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  • update ocz vertex le capacity via firmware update

    - by Ben Voigt
    I have an OCZ Vertex LE 100GB drive. It's actually 128GiB of NAND flash, with a whopping 28%+ reserved for write combining. Most 128GiB drives are actually ~ 115GB usable (and marketed as 120GB or 128GB). There were rumors that the reserved fraction could be decreased on OCZ 100GB drives. Can anyone provide a link to firmware that does that, or an official statement that no such firmware exists? (NB: I recently installed the 1.24 firmware from the OCZ site, it didn't affect the capacity. Possibly because the rumors say the capacity change is destructive to existing content.) Of possible interest: flashing firmware was more of a pain than it should have been -- the tool didn't detect the disk until I booted an older Windows install off a secondary hard disk, I suspect the Intel SATA driver is the issue and tool only works with the msachi.sys driver.

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