11/1/2019 Best Matlab Mac - And Reviews
Here are some benchmarks I obtained using. Note that my Mac Book Air (the last entry) has only 2GB memory. Windows 7 (64-bit) 2.66 GHz Intel Core 2 Quad 4 threads, Windows 7 Enterprise, 2.66 GHz Intel Core 2 Quad, 4 GB memory, NVidia Quadro FX 1500, MATLAB 7.11.0.514 (R2010b) 2. Windows XP (32-bit) 2.66 GHz Intel Core 2 2 threads, Windows XP SP3, 2.66 GHz Intel Core 2 CPU 6700, 3.25 GB memory, NVidia Quadro FX 1500, MATLAB 7.11.0.514 (R2010b) 3. Windows Server 2003 (64-bit) 2.4 GHz AMD Opteron 8 threads, Windows Server 2003 Standard x64 Edition SP2, 2.4 GHz AMD Opteron, 32 GB memory, Rage XL PCI, MATLAB 7.11.0.514 (R2010b) 4.
Windows 7 (64-bit) 2.39 GHz Intel Xeon 16 threads, Windows 7 Enterprise x64, 2.39 GHz Intel Xeon E5520, 12 GB memory, NVidia GeForce 9500 GT, MATLAB 7.11.0.514 (R2010b) 5. Windows 7 (32-bit) 1.6 GHz Intel Atom 1 thread, Windows 7, Intel Atom CPU N270 @1.60 GHz, 1.0 GB memory, Mobile Intel® 945 Express Chipset Family, MATLAB 7.11.0.514 (R2010b) 6. Mac 10.5 (64-bit) 2.66 GHz Intel Xeon 4 threads, Leopard 10.5.5, dual 2.66 GHz Intel Xeon, 4 GB memory, NVidia GeForce 7300GT, MATLAB 7.11.0.514 (R2010b) 7. Mac 10.6 (64-bit) 2.27 GHz Intel Xeon 16 threads, Snow Leopard 10.6.2, 2.37 GHz Intel Xeon E5520, 12 GB memory, NVidia GeForce 9500 GT, MATLAB 7.11.0.514 (R2010b) 8.
Linux Debian (64-bit) 2.66 GHz Intel Core 2 2 threads, 2.6.26-2-amd64 kernel, 2.66 GHz Intel Core 2 CPU 6700, 4 GB memory, NVidia Quadro FX 1500, MATLAB 7.11.0.514 (R2010b) 9. Linux Ubuntu (64-bit) 2.27 GHz Intel Xeon 16 threads, 2.6.30-020630-generic kernel, 2.37 GHz Intel Xeon E5520, 12 GB memory, NVidia GeForce 9500 GT, MATLAB 7.11.0.514 (R2010b) 10. MacBookAir 10.6 (64-bit) 1.86 GHz Intel Core 2 4 threads, Snow Leopard 10.6.7, 1.86 GHz Intel Core 2, 2 GB memory, NVIDIA GeForce 9400M, MATLAB Version 7.11.0.584 (R2010b) LU FFT ODE Sparse 2-D 3-D 1. 0.0577 0.1153 0.1641 0.2628 0.5081 0.7387 2. 0.0775 0.1132 0.1199 0.2364 0.3300 0.5703 3.
0.1042 0.1348 0.2572 0.7519 0.5510 0.4986 4. 0.0386 0.0546 0.1415 0.2079 0.4124 0.7271 5. 1.8844 0.7826 0.7091 1.5299 2.4052 1.9797 6.
0.0760 0.1770 0.1817 0.3503 0.5827 0.6359 7. 0.0465 0.0995 0.1892 0.3535 0.4546 0.6841 8. 0.0658 0.0945 0.1636 0.2567 0.2651 0.2044 9.
0.0515 0.0434 0.1727 0.2814 1.3914 1.6377 10. 0.0866 0.1767 0.2660 0.3801 1.0964 1.3405. I'm afraid I don't see much point in having 1066 MHz DDR3 memory with an 800 MHz Front Side Bus. 2 GB really isn't enough memory to run any MATLAB version that is currently purchasable. On Mac, I don't think I'd be comfortable even with the maximum 4 GB you can equip the devices with. 1.4 GHz isn't very fast, even if you run both cores.
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The desktop system I'm using at the moment is 4 GHz single core Pentium 4, a two or three generation hand-me-down because no-one else thought it fast enough to bother with; if you are accustomed to running systems designed this century, you likely won't find 1.4 GHz an enjoyable experience for computation. The graphics aren't anything special. The 320M integrated chipset has 32 shader cores and no dedicated memory, using 256 MB of the system memory (reducing the amount available for MATLAB). See for some benchmarks It is difficult to findy any official nvidea acknowledgement that the 320M exists in its CUDA information, but the suggests that the 320M supports Cuda 1.2 capabilities (not enough for MATLAB to use, but enough for Accelereyes Jacket to use); that evidence also indicates 48 CUDA cores, not the 32 Apple officially claims. If your question is whether MATLAB will run on the Airbook: then Yes, but I would push the 4 GB memory in ASAP.
Download Matlab Mac
Compared to what? I did some research earlier this year, after the latest generation MacBook Pro; I found that it was no longer possible to buy any MacBook Pro with a graphics card that supported CUDA 2.0 or later. It was possible to buy CUDA cards (one from Apple directly, one from a third party with Apple blessing) for the G5 Server. The 320M was pretty typical of the graphics capability of last year's MacBook generation; this year Apple went for completely different graphics that were not from NVIDIA at all, including one MacBook Pro that used graphics integrated in to the Intel CPU. If you look at one of the MacBook Pro or even iMAC line that has an Intel i5 or i7 in it, you probably will not be getting even CUDA 1.2 capability or any ability to purchase a CUDA 2.0 compatible graphics card like used to be available. Anyhow, CUDA is not necessarily important for you. I was looking to replace my old burnt-out Windows system and I was thinking of buying in to CUDA 2.0 capabilities so I could run SETI@Home and the like, and so that I could start studying up on the topic; what I determined was that I couldn't do it for less than $6000 in a Mac these days, but that for half that price I could get a very decent Linux system from a firm such as liquidnitrogenoverclocking.com.
I'd.prefer. not to be driven back to PC hardware, but unfortunately it appears that Apple is getting more and more in to the 'computer experience' and less and less in to high performance computing:(. I decided to read a little about CUDA/MatLab since I really had no idea what it was. Anyway, what I gather is that this allows for some processing to be performed on the graphics card as well as the CPU.
Is this basically the idea. Also, in general MatLab typically only uses the CPU and CUDA is utilized only with certain toolbaoxes, can't remember the box. At any rate, you are correct. There are MacBook Pros that use integrated video and if I were to upgrade my purchase to a pro, I would not go for the IV. It looks like I'll be purchasing the MacBook Pro and not the Air. Kind of stinks as I was really likeing the Airs.
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Yes, CUDA is the use of the graphics card (or similar architecture) to perform parallel computations. The CUDA instruction set is limited, but what it can do it can do pretty quickly. If you are interested in 'high performance' computations then you probably want to investigate CUDA.
CUDA use does require special code, or at least code operating on a special class of objects; as far as I know it is not integrated in to any of the other toolboxes. Is it possible for you to get access to the kind of Air that you would be interested in purchasing? With access and a MATLAB trial license, you could see for yourself whether performance was satisfactory for your purposes. 5 hours or 7 hours for the battery, though. Wonder if that includes the situation where you are doing heavy computing on it? Some follow-up thoughts: I think the most important comment that Walter made was 'compared to what?'
To which I would add, 'For what purpose?' It seems a bit silly to compare this laptop to industrial-grade desktops. Most people considering a Mac Book Air are not in the market for 4GHz hand-me-downs, let alone the current top models. The Mac Book Air should be compared to other laptops.
Similarly, there are a lot of uses of MATLAB that don't require blazing speeds or even 4GB memory. I use the Air when I'm traveling, and most of the time I don't miss the extra power. Balance that against the ease of carrying it around. One major caveat: Very few hotels have good wireless support, and this caused me no end of trouble for a while.
Fortunately, there is a cheap solution. If you are traveling, get an Ethernet-to-USB adapter!!!!
MATLAB Student Version Release 13 is now available from, a developer of technical computing software for engineers and scientists. The release includes the latest updates to the company's MATLAB and Simulink products for Mac OS X, Windows, and Linux systems. MATLAB is an integrated technical computing environment that combines numeric computation, advanced graphics and visualization and a high-level programming language. MATLAB includes functions for data analysis and visualization; numeric and symbolic computation; engineering and scientific graphics; modeling, simulation and prototyping; programming, application development; and GUI (graphical user interface) design. MATLAB is used in a variety of application areas including signal and image processing, control system design, financial engineering, and medical research. The open architecture makes it easy to use MATLAB and companion products to explore data and create custom tools that provide early insights and competitive advantages, according to The Mathworks.
MATLAB Student Version is a complete software package that includes MATLAB 6.5, Simulink 5, and the Symbolic Math Toolbox. Simulink 5 provides access to multi-domain capabilities for modeling, simulating, and analyzing mechanical, electrical power, and wireless systems.
A number of application-specific companion products are also available for students. In addition to the release of MATLAB Student Version, The MathWorks has launched the. The site's Student Center offers students step-by-step tutorials that outline how to get started using MATLAB and Simulink, links to global job opportunities that require MATLAB skills, and information on companion software. The Faculty Center, a resource for educators, provides links to MATLAB-based course materials and recommended product groups, as well as listings of the more than 500 MATLAB-based textbooks currently in print. MATLAB Student Version is available immediately at a discounted student price of US$99 in the U.S. And Canada, and can be purchased.
See The MathWorks site for info on pricing elsewhere. To qualify for purchase, students must be enrolled at a degree-granting institution and have a valid student ID. See the for details. This story, 'MATLAB Student Version Release 13 available' was originally published.
There are quite a few software applications that are considered indispensable for a variety of specialized subject areas of academia and research, such as engineering, science, and economics. And the popular MATLAB is an ideal example of that. Built around the proprietary scripting languge of the same name, the MATLAB environment is used by millions of users for their numerical computing needs, with advanced usage cases including creation of user interfaces, and even interfacing with programs written in other languages.
And if it’s really that awesome, MATLAB must be the only one of its kind, right? Wrong, as there are many great MATLAB alternatives that you can use. Cara setting router wifi cisco first media. So let’s wait no more, and jump right in!
Best MATLAB Alternatives 1. Wolfram Mathematica Deveoped by Wolfram Research, the pioneers of computational software, Mathematica comes with a truckload of features for all your mathematical computational needs.
The latest version boasts over 700 new functions, as well as multiple function libraries and geo visualization/animation tools. And that’s just the tip of the iceberg.
From 2D/3D image processing to enhanced signal processing, and from automated machine learning to enhanced graphical computations, Mathematica can handle it all. Then there are powerful tools for connecting to the systems based on DLL, SQL, CUDA, OpenCL, and Fortran. A curated API framework allows for external device connectivity, and it can also connect with programming languages like Haskell, AppleScript, and Clojure. Platform Availability: Windows, Mac OS X, Linux Pricing: Starts at $149 per year. Pricing varies according to usage cases. Sniper ultimate kill 2017 - full movie.
15 days trial available 2. Maple Having a powerful Math engine, Maple is a pretty feature heavy MATLAB alternative. It lets you enter problems in traditional mathematical notation, and allows creation of custom interfaces. Maple includes a dynamically typed, imperative-style programming language, identical to Pascal. And of course, it can interface with other languages (e.g. C, Java) as well. It has over 5000 functions covering every area of mathematics, and numerous useful modules like equation editor, variable manager, and live-data plots.
Even hand written symbols are recognized, and it can connect with MATLAB as well. Thanks to its versatility, Maple’s usage extends in areas like financial modeling, control design, and statistical data analysis.
In fact, it was of the mathematics based Gomboc shape. Platform Availability: Windows, Mac OS X, Linux Pricing: Starts at $99.
Pricing varies according to usage cases. 30 days trial available 3. GNU Octave Want a powerhouse free and open-source alternative to MATLAB? GNU Octave is all you need. Comprised of a high-level interpreted language, it’s primarily meant for numerical computations, but can also handle extensive data manipulation and visualization just as good. Probably one of its most unique features is the interactive command line interface, used for solving linear and non-linear problems numerically. Moreover, the Octave language is quite identical to MATLAB, which makes for easy portability of most programs.
Other notable features include command history, variable name completion, and a, which let the software do everything from solving Differential Advection Reaction (DAR) Partial Differential Equations, to interfacing with SQL databases. Platform Availability: Windows, Mac OS X, Linux Pricing: Free 4. Scilab Maintaining a healthy balance between features and ease of use, Scilab is a great open-source numerical computational package, that you can use in place of MATLAB. It comes with a high-level, numerically oriented programming language. However, due to features such as dynamic typing and automatic memory management, it becomes possible to express numerical problems using reduced amount of code. Scilab also comes with a free “Xcos” package (analogous to MATLAB’s Simulink) for effortless modeling and simulation of explicit and implicit dynamical systems. The syntax of Scilab is largely based on MATLAB.
Other regular functionalities, such as 2D/3D visualization, control system analysis etc. Are there too. Oh, and there are quite a few available for it too, for doing things like network computations and GPU computing. Platform Availability: Windows, Mac OS X, Linux Pricing: Free 5. SageMath Covering just about everything in mathematics (algebra, calculus, number theory etc.), SageMath is a robust numerical computing software that uses a Python like syntax, with support for procedural, functional, and object oriented constructs.
Perhaps its standout feature is a browser based notebook that lets you review and re-use previous inputs/outputs, including everything from graphics to text annotations. It’s compatible with almost all major, and the notebooks can be accessed both locally, and over secure HTTP connections.
Its Python standard library includes tools for connecting to SQL, FTP etc., and SageMatch can be even called from within Mathematica (mentioned previously). It’s built from nearly 100, with distributed computing support and an automated test suite rounding up the feature list. Platform Availability: Windows, Mac OS X, Linux Pricing: Free 6. Julia Strictly speaking, Julia is not a full “alternative” to MATLAB, in the sense that it’s essentially a high-level, dynamic programming language, intended for numerical computing. However, you can easily use it via the free. As for the language itself, it comes with a sophisticated compiler, with support for distributed parallel computing, and a large mathematical function library.
And thanks to the active developer community, the list of is steadily growing too. It’s designed for cloud computing, and the “multiple dispatch” approach makes it easy to d efine function behavior across many argument type combinations. You also get Lisp like macros, shell-like capabilities for easily managing other processes. Oh, and are held at none other than the world-renowned MIT itself. Platform Availability: Windows, Mac OS X, Linux Pricing: Free SEE ALSO: Do numerical computation better, even without MATLAB As the undisputed leader when it comes to numerical/mathematical computing, MATLAB is obviously the most feature loaded computational package out there. But if you’re looking for something different, there are quite a few great alternatives available, as evinced above. Try them out, and sound off your findings in the comments section below.
Here are some benchmarks I obtained using. Note that my Mac Book Air (the last entry) has only 2GB memory. Windows 7 (64-bit) 2.66 GHz Intel Core 2 Quad 4 threads, Windows 7 Enterprise, 2.66 GHz Intel Core 2 Quad, 4 GB memory, NVidia Quadro FX 1500, MATLAB 7.11.0.514 (R2010b) 2. Windows XP (32-bit) 2.66 GHz Intel Core 2 2 threads, Windows XP SP3, 2.66 GHz Intel Core 2 CPU 6700, 3.25 GB memory, NVidia Quadro FX 1500, MATLAB 7.11.0.514 (R2010b) 3. Windows Server 2003 (64-bit) 2.4 GHz AMD Opteron 8 threads, Windows Server 2003 Standard x64 Edition SP2, 2.4 GHz AMD Opteron, 32 GB memory, Rage XL PCI, MATLAB 7.11.0.514 (R2010b) 4. Windows 7 (64-bit) 2.39 GHz Intel Xeon 16 threads, Windows 7 Enterprise x64, 2.39 GHz Intel Xeon E5520, 12 GB memory, NVidia GeForce 9500 GT, MATLAB 7.11.0.514 (R2010b) 5. Windows 7 (32-bit) 1.6 GHz Intel Atom 1 thread, Windows 7, Intel Atom CPU N270 @1.60 GHz, 1.0 GB memory, Mobile Intel® 945 Express Chipset Family, MATLAB 7.11.0.514 (R2010b) 6.
Mac 10.5 (64-bit) 2.66 GHz Intel Xeon 4 threads, Leopard 10.5.5, dual 2.66 GHz Intel Xeon, 4 GB memory, NVidia GeForce 7300GT, MATLAB 7.11.0.514 (R2010b) 7. Mac 10.6 (64-bit) 2.27 GHz Intel Xeon 16 threads, Snow Leopard 10.6.2, 2.37 GHz Intel Xeon E5520, 12 GB memory, NVidia GeForce 9500 GT, MATLAB 7.11.0.514 (R2010b) 8. Linux Debian (64-bit) 2.66 GHz Intel Core 2 2 threads, 2.6.26-2-amd64 kernel, 2.66 GHz Intel Core 2 CPU 6700, 4 GB memory, NVidia Quadro FX 1500, MATLAB 7.11.0.514 (R2010b) 9. Linux Ubuntu (64-bit) 2.27 GHz Intel Xeon 16 threads, 2.6.30-020630-generic kernel, 2.37 GHz Intel Xeon E5520, 12 GB memory, NVidia GeForce 9500 GT, MATLAB 7.11.0.514 (R2010b) 10. MacBookAir 10.6 (64-bit) 1.86 GHz Intel Core 2 4 threads, Snow Leopard 10.6.7, 1.86 GHz Intel Core 2, 2 GB memory, NVIDIA GeForce 9400M, MATLAB Version 7.11.0.584 (R2010b) LU FFT ODE Sparse 2-D 3-D 1. 0.0577 0.1153 0.1641 0.2628 0.5081 0.7387 2.
0.0775 0.1132 0.1199 0.2364 0.3300 0.5703 3. 0.1042 0.1348 0.2572 0.7519 0.5510 0.4986 4. 0.0386 0.0546 0.1415 0.2079 0.4124 0.7271 5. 1.8844 0.7826 0.7091 1.5299 2.4052 1.9797 6. 0.0760 0.1770 0.1817 0.3503 0.5827 0.6359 7. 0.0465 0.0995 0.1892 0.3535 0.4546 0.6841 8. 0.0658 0.0945 0.1636 0.2567 0.2651 0.2044 9.
0.0515 0.0434 0.1727 0.2814 1.3914 1.6377 10. 0.0866 0.1767 0.2660 0.3801 1.0964 1.3405. I'm afraid I don't see much point in having 1066 MHz DDR3 memory with an 800 MHz Front Side Bus. 2 GB really isn't enough memory to run any MATLAB version that is currently purchasable. On Mac, I don't think I'd be comfortable even with the maximum 4 GB you can equip the devices with. 1.4 GHz isn't very fast, even if you run both cores. The desktop system I'm using at the moment is 4 GHz single core Pentium 4, a two or three generation hand-me-down because no-one else thought it fast enough to bother with; if you are accustomed to running systems designed this century, you likely won't find 1.4 GHz an enjoyable experience for computation.
The graphics aren't anything special. The 320M integrated chipset has 32 shader cores and no dedicated memory, using 256 MB of the system memory (reducing the amount available for MATLAB). See for some benchmarks It is difficult to findy any official nvidea acknowledgement that the 320M exists in its CUDA information, but the suggests that the 320M supports Cuda 1.2 capabilities (not enough for MATLAB to use, but enough for Accelereyes Jacket to use); that evidence also indicates 48 CUDA cores, not the 32 Apple officially claims.
If your question is whether MATLAB will run on the Airbook: then Yes, but I would push the 4 GB memory in ASAP. Compared to what? I did some research earlier this year, after the latest generation MacBook Pro; I found that it was no longer possible to buy any MacBook Pro with a graphics card that supported CUDA 2.0 or later.
It was possible to buy CUDA cards (one from Apple directly, one from a third party with Apple blessing) for the G5 Server. The 320M was pretty typical of the graphics capability of last year's MacBook generation; this year Apple went for completely different graphics that were not from NVIDIA at all, including one MacBook Pro that used graphics integrated in to the Intel CPU. If you look at one of the MacBook Pro or even iMAC line that has an Intel i5 or i7 in it, you probably will not be getting even CUDA 1.2 capability or any ability to purchase a CUDA 2.0 compatible graphics card like used to be available. Anyhow, CUDA is not necessarily important for you. I was looking to replace my old burnt-out Windows system and I was thinking of buying in to CUDA 2.0 capabilities so I could run SETI@Home and the like, and so that I could start studying up on the topic; what I determined was that I couldn't do it for less than $6000 in a Mac these days, but that for half that price I could get a very decent Linux system from a firm such as liquidnitrogenoverclocking.com.
I'd.prefer. not to be driven back to PC hardware, but unfortunately it appears that Apple is getting more and more in to the 'computer experience' and less and less in to high performance computing:(. I decided to read a little about CUDA/MatLab since I really had no idea what it was. Anyway, what I gather is that this allows for some processing to be performed on the graphics card as well as the CPU. Is this basically the idea.
Also, in general MatLab typically only uses the CPU and CUDA is utilized only with certain toolbaoxes, can't remember the box. At any rate, you are correct.
There are MacBook Pros that use integrated video and if I were to upgrade my purchase to a pro, I would not go for the IV. It looks like I'll be purchasing the MacBook Pro and not the Air.
Kind of stinks as I was really likeing the Airs. Yes, CUDA is the use of the graphics card (or similar architecture) to perform parallel computations. The CUDA instruction set is limited, but what it can do it can do pretty quickly.
If you are interested in 'high performance' computations then you probably want to investigate CUDA. CUDA use does require special code, or at least code operating on a special class of objects; as far as I know it is not integrated in to any of the other toolboxes. Is it possible for you to get access to the kind of Air that you would be interested in purchasing?
With access and a MATLAB trial license, you could see for yourself whether performance was satisfactory for your purposes. 5 hours or 7 hours for the battery, though. Wonder if that includes the situation where you are doing heavy computing on it? Some follow-up thoughts: I think the most important comment that Walter made was 'compared to what?' To which I would add, 'For what purpose?' It seems a bit silly to compare this laptop to industrial-grade desktops.
Most people considering a Mac Book Air are not in the market for 4GHz hand-me-downs, let alone the current top models. The Mac Book Air should be compared to other laptops.
Similarly, there are a lot of uses of MATLAB that don't require blazing speeds or even 4GB memory. I use the Air when I'm traveling, and most of the time I don't miss the extra power. Balance that against the ease of carrying it around. One major caveat: Very few hotels have good wireless support, and this caused me no end of trouble for a while. Fortunately, there is a cheap solution.
If you are traveling, get an Ethernet-to-USB adapter!!!!
![]()
Windows 7 (64-bit) 2.39 GHz Intel Xeon 16 threads, Windows 7 Enterprise x64, 2.39 GHz Intel Xeon E5520, 12 GB memory, NVidia GeForce 9500 GT, MATLAB 7.11.0.514 (R2010b) 5. Windows 7 (32-bit) 1.6 GHz Intel Atom 1 thread, Windows 7, Intel Atom CPU N270 @1.60 GHz, 1.0 GB memory, Mobile Intel® 945 Express Chipset Family, MATLAB 7.11.0.514 (R2010b) 6. Mac 10.5 (64-bit) 2.66 GHz Intel Xeon 4 threads, Leopard 10.5.5, dual 2.66 GHz Intel Xeon, 4 GB memory, NVidia GeForce 7300GT, MATLAB 7.11.0.514 (R2010b) 7. Mac 10.6 (64-bit) 2.27 GHz Intel Xeon 16 threads, Snow Leopard 10.6.2, 2.37 GHz Intel Xeon E5520, 12 GB memory, NVidia GeForce 9500 GT, MATLAB 7.11.0.514 (R2010b) 8. Linux Debian (64-bit) 2.66 GHz Intel Core 2 2 threads, 2.6.26-2-amd64 kernel, 2.66 GHz Intel Core 2 CPU 6700, 4 GB memory, NVidia Quadro FX 1500, MATLAB 7.11.0.514 (R2010b) 9. Linux Ubuntu (64-bit) 2.27 GHz Intel Xeon 16 threads, 2.6.30-020630-generic kernel, 2.37 GHz Intel Xeon E5520, 12 GB memory, NVidia GeForce 9500 GT, MATLAB 7.11.0.514 (R2010b) 10. MacBookAir 10.6 (64-bit) 1.86 GHz Intel Core 2 4 threads, Snow Leopard 10.6.7, 1.86 GHz Intel Core 2, 2 GB memory, NVIDIA GeForce 9400M, MATLAB Version 7.11.0.584 (R2010b) LU FFT ODE Sparse 2-D 3-D 1.
0.0577 0.1153 0.1641 0.2628 0.5081 0.7387 2. 0.0775 0.1132 0.1199 0.2364 0.3300 0.5703 3. 0.1042 0.1348 0.2572 0.7519 0.5510 0.4986 4. 0.0386 0.0546 0.1415 0.2079 0.4124 0.7271 5.
1.8844 0.7826 0.7091 1.5299 2.4052 1.9797 6. 0.0760 0.1770 0.1817 0.3503 0.5827 0.6359 7.
0.0465 0.0995 0.1892 0.3535 0.4546 0.6841 8. 0.0658 0.0945 0.1636 0.2567 0.2651 0.2044 9. 0.0515 0.0434 0.1727 0.2814 1.3914 1.6377 10. 0.0866 0.1767 0.2660 0.3801 1.0964 1.3405. I'm afraid I don't see much point in having 1066 MHz DDR3 memory with an 800 MHz Front Side Bus.
2 GB really isn't enough memory to run any MATLAB version that is currently purchasable. On Mac, I don't think I'd be comfortable even with the maximum 4 GB you can equip the devices with.
1.4 GHz isn't very fast, even if you run both cores. The desktop system I'm using at the moment is 4 GHz single core Pentium 4, a two or three generation hand-me-down because no-one else thought it fast enough to bother with; if you are accustomed to running systems designed this century, you likely won't find 1.4 GHz an enjoyable experience for computation. The graphics aren't anything special. The 320M integrated chipset has 32 shader cores and no dedicated memory, using 256 MB of the system memory (reducing the amount available for MATLAB). See for some benchmarks It is difficult to findy any official nvidea acknowledgement that the 320M exists in its CUDA information, but the suggests that the 320M supports Cuda 1.2 capabilities (not enough for MATLAB to use, but enough for Accelereyes Jacket to use); that evidence also indicates 48 CUDA cores, not the 32 Apple officially claims.
If your question is whether MATLAB will run on the Airbook: then Yes, but I would push the 4 GB memory in ASAP. Compared to what? I did some research earlier this year, after the latest generation MacBook Pro; I found that it was no longer possible to buy any MacBook Pro with a graphics card that supported CUDA 2.0 or later. It was possible to buy CUDA cards (one from Apple directly, one from a third party with Apple blessing) for the G5 Server. The 320M was pretty typical of the graphics capability of last year's MacBook generation; this year Apple went for completely different graphics that were not from NVIDIA at all, including one MacBook Pro that used graphics integrated in to the Intel CPU. If you look at one of the MacBook Pro or even iMAC line that has an Intel i5 or i7 in it, you probably will not be getting even CUDA 1.2 capability or any ability to purchase a CUDA 2.0 compatible graphics card like used to be available. Anyhow, CUDA is not necessarily important for you.
I was looking to replace my old burnt-out Windows system and I was thinking of buying in to CUDA 2.0 capabilities so I could run SETI@Home and the like, and so that I could start studying up on the topic; what I determined was that I couldn't do it for less than $6000 in a Mac these days, but that for half that price I could get a very decent Linux system from a firm such as liquidnitrogenoverclocking.com. I'd.prefer. not to be driven back to PC hardware, but unfortunately it appears that Apple is getting more and more in to the 'computer experience' and less and less in to high performance computing:(. I decided to read a little about CUDA/MatLab since I really had no idea what it was. Anyway, what I gather is that this allows for some processing to be performed on the graphics card as well as the CPU.
Is this basically the idea. Also, in general MatLab typically only uses the CPU and CUDA is utilized only with certain toolbaoxes, can't remember the box. At any rate, you are correct. There are MacBook Pros that use integrated video and if I were to upgrade my purchase to a pro, I would not go for the IV. It looks like I'll be purchasing the MacBook Pro and not the Air. Kind of stinks as I was really likeing the Airs. Yes, CUDA is the use of the graphics card (or similar architecture) to perform parallel computations.
The CUDA instruction set is limited, but what it can do it can do pretty quickly. If you are interested in 'high performance' computations then you probably want to investigate CUDA. CUDA use does require special code, or at least code operating on a special class of objects; as far as I know it is not integrated in to any of the other toolboxes. Is it possible for you to get access to the kind of Air that you would be interested in purchasing?
With access and a MATLAB trial license, you could see for yourself whether performance was satisfactory for your purposes. 5 hours or 7 hours for the battery, though. Wonder if that includes the situation where you are doing heavy computing on it? Some follow-up thoughts: I think the most important comment that Walter made was 'compared to what?' To which I would add, 'For what purpose?' It seems a bit silly to compare this laptop to industrial-grade desktops.
Most people considering a Mac Book Air are not in the market for 4GHz hand-me-downs, let alone the current top models. The Mac Book Air should be compared to other laptops. Similarly, there are a lot of uses of MATLAB that don't require blazing speeds or even 4GB memory. I use the Air when I'm traveling, and most of the time I don't miss the extra power. Balance that against the ease of carrying it around. One major caveat: Very few hotels have good wireless support, and this caused me no end of trouble for a while.
Fortunately, there is a cheap solution. If you are traveling, get an Ethernet-to-USB adapter!!!!
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