What computer would be fastest for Mathematica Home Edition?Which processor and graphics card to use for my Mathematica licenseBest Mac for MathematicaWhat is the fastest way to find an integer-valued row echelon form for a matrix with integer entries?Launching remote kernels - will it help RAM needs?What is the fastest way to maintain a large set of expressions?Making simultaneous equation solving more efficientBenchmarking Mathematica performance on the Raspberry Pistrange timing behaviorWhat is the fastest way to compute digits of $pi$ using Mathematica?What is the fastest way to simplify $sqrta^2$ for $a>0$How many cores can I use with my license?How can I speed up Mathematica calculations?
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What computer would be fastest for Mathematica Home Edition?
Which processor and graphics card to use for my Mathematica licenseBest Mac for MathematicaWhat is the fastest way to find an integer-valued row echelon form for a matrix with integer entries?Launching remote kernels - will it help RAM needs?What is the fastest way to maintain a large set of expressions?Making simultaneous equation solving more efficientBenchmarking Mathematica performance on the Raspberry Pistrange timing behaviorWhat is the fastest way to compute digits of $pi$ using Mathematica?What is the fastest way to simplify $sqrta^2$ for $a>0$How many cores can I use with my license?How can I speed up Mathematica calculations?
$begingroup$
I am planning the purchase of a new computer for use with Mathematica Home Edition, which is limited, as far as I understand, to two simultaneous kernels. Can anyone recommend particular hardware?
performance-tuning computer cpu
edited Apr 14 at 15:02
Henrik Schumacher
63.2k587176
asked Apr 14 at 13:47
Ralph DratmanRalph Dratman
870819
$endgroup$
add a comment |
$begingroup$
I am planning the purchase of a new computer for use with Mathematica Home Edition, which is limited, as far as I understand, to two simultaneous kernels. Can anyone recommend particular hardware?
performance-tuning computer cpu
edited Apr 14 at 15:02
Henrik Schumacher
63.2k587176
asked Apr 14 at 13:47
Ralph DratmanRalph Dratman
870819
$endgroup$
$begingroup$
Related: Best Mac for Mathematica and Which processor and graphics card to use for my Mathematica license
$endgroup$
– C. E.
Apr 14 at 19:13
1
$begingroup$
Common recommendations are to get a lot of RAM and SSD.
$endgroup$
– C. E.
Apr 14 at 19:15
$begingroup$
I agree with comment above! RAM and drive size is a definite consideration issue. I recently purchased a new MacBook Pro 15" with 16 MB RAM and 1 TB SSD. I regularly perform analysis on time based data files that are hundreds of mega bytes in size. This MacBook Pro is better than my work desktop system. The biggest issue for me is RAM. I need to get a lot data in memory where I can perform the Mathematica operations. I say go with as much RAM, drive, and processor as your wallet will allow. Mathematica runs great on MacOS.
$endgroup$
– Joseph
Apr 14 at 19:35
add a comment |
$begingroup$
I am planning the purchase of a new computer for use with Mathematica Home Edition, which is limited, as far as I understand, to two simultaneous kernels. Can anyone recommend particular hardware?
performance-tuning computer cpu
edited Apr 14 at 15:02
Henrik Schumacher
63.2k587176
asked Apr 14 at 13:47
Ralph DratmanRalph Dratman
870819
$endgroup$
I am planning the purchase of a new computer for use with Mathematica Home Edition, which is limited, as far as I understand, to two simultaneous kernels. Can anyone recommend particular hardware?
performance-tuning computer cpu
performance-tuning computer cpu
edited Apr 14 at 15:02
Henrik Schumacher
63.2k587176
asked Apr 14 at 13:47
Ralph DratmanRalph Dratman
870819
edited Apr 14 at 15:02
Henrik Schumacher
63.2k587176
asked Apr 14 at 13:47
Ralph DratmanRalph Dratman
870819
edited Apr 14 at 15:02
Henrik Schumacher
63.2k587176
edited Apr 14 at 15:02
Henrik Schumacher
63.2k587176
edited Apr 14 at 15:02
Henrik Schumacher
63.2k587176
63.2k587176
asked Apr 14 at 13:47
Ralph DratmanRalph Dratman
870819
asked Apr 14 at 13:47
Ralph DratmanRalph Dratman
870819
asked Apr 14 at 13:47
Ralph DratmanRalph Dratman
870819
870819
$begingroup$
Related: Best Mac for Mathematica and Which processor and graphics card to use for my Mathematica license
$endgroup$
– C. E.
Apr 14 at 19:13
1
$begingroup$
Common recommendations are to get a lot of RAM and SSD.
$endgroup$
– C. E.
Apr 14 at 19:15
$begingroup$
I agree with comment above! RAM and drive size is a definite consideration issue. I recently purchased a new MacBook Pro 15" with 16 MB RAM and 1 TB SSD. I regularly perform analysis on time based data files that are hundreds of mega bytes in size. This MacBook Pro is better than my work desktop system. The biggest issue for me is RAM. I need to get a lot data in memory where I can perform the Mathematica operations. I say go with as much RAM, drive, and processor as your wallet will allow. Mathematica runs great on MacOS.
$endgroup$
– Joseph
Apr 14 at 19:35
add a comment |
$begingroup$
Related: Best Mac for Mathematica and Which processor and graphics card to use for my Mathematica license
$endgroup$
– C. E.
Apr 14 at 19:13
1
$begingroup$
Common recommendations are to get a lot of RAM and SSD.
$endgroup$
– C. E.
Apr 14 at 19:15
$begingroup$
I agree with comment above! RAM and drive size is a definite consideration issue. I recently purchased a new MacBook Pro 15" with 16 MB RAM and 1 TB SSD. I regularly perform analysis on time based data files that are hundreds of mega bytes in size. This MacBook Pro is better than my work desktop system. The biggest issue for me is RAM. I need to get a lot data in memory where I can perform the Mathematica operations. I say go with as much RAM, drive, and processor as your wallet will allow. Mathematica runs great on MacOS.
$endgroup$
– Joseph
Apr 14 at 19:35
$begingroup$
Related: Best Mac for Mathematica and Which processor and graphics card to use for my Mathematica license
$endgroup$
– C. E.
Apr 14 at 19:13
$begingroup$
Related: Best Mac for Mathematica and Which processor and graphics card to use for my Mathematica license
$endgroup$
– C. E.
Apr 14 at 19:13
1
1
$begingroup$
Common recommendations are to get a lot of RAM and SSD.
$endgroup$
– C. E.
Apr 14 at 19:15
$begingroup$
Common recommendations are to get a lot of RAM and SSD.
$endgroup$
– C. E.
Apr 14 at 19:15
$begingroup$
I agree with comment above! RAM and drive size is a definite consideration issue. I recently purchased a new MacBook Pro 15" with 16 MB RAM and 1 TB SSD. I regularly perform analysis on time based data files that are hundreds of mega bytes in size. This MacBook Pro is better than my work desktop system. The biggest issue for me is RAM. I need to get a lot data in memory where I can perform the Mathematica operations. I say go with as much RAM, drive, and processor as your wallet will allow. Mathematica runs great on MacOS.
$endgroup$
– Joseph
Apr 14 at 19:35
$begingroup$
I agree with comment above! RAM and drive size is a definite consideration issue. I recently purchased a new MacBook Pro 15" with 16 MB RAM and 1 TB SSD. I regularly perform analysis on time based data files that are hundreds of mega bytes in size. This MacBook Pro is better than my work desktop system. The biggest issue for me is RAM. I need to get a lot data in memory where I can perform the Mathematica operations. I say go with as much RAM, drive, and processor as your wallet will allow. Mathematica runs great on MacOS.
$endgroup$
– Joseph
Apr 14 at 19:35
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
It is right, you can run only two Mathematica kernels at once in the Home Edition. But whether this is an actual limitation (and whether you should prefer a CPU with high single-core throughput) depends on what you are about to do with Mathematica.
I can only speak for my personal scope of application which is numerics. Here I seldomly find the Parallel-framework helpful, as Compiled code with options RuntimeAttributes -> Listable and Parallelization -> True usually scales much better than Parallel and friends. Such parallelized code is sped up by Intel MKL (and OpenMP?) outside of the Mathematica kernel. Even better is to use vectorized code for linear algebra; such is accelerated also by Intel MKL and such low level libraries as BLAS. The hardware ressources used are also independent of the number of Mathematica kernels.
So, for numerical computations, you may enjoy mostly all the benefits of a fat multi core CPU, even with the Home Edition.
Things might be different for vast symbolic computations, though, or when you are forced to run slow built-in functions that are not parallelized themselves and that you cannot reimplement yourselves more efficiently.
If you are interested in neural network facilities, you might prefer NVidia graphics cards over those by AMD since only CUDA, not OpenCL is supported. Anyways, most algorithms in Mathematica are not optimized for GPUs, so apart from the very localized application of neural networks, also the particular choice of GPU does not matter much.
In think the only general advice that one can give is: Having lots of memory is always a good idea and does not harm in any case. Apart from that, I doubt that it is a good idea to build your new machine depending on your Mathematica license.
Addendum
A propos Intel MKL: I really hate to say this (and it might cost me further downvotes), but as far as numerics in Mathematica are considered, one should keep in mind that Intel CPUs might be preferable over AMD CPUs just because Intel MKL is optimized for Intel hardware (and rumor has it that AMD processors are nerfed in MKL). I cannot quantify the difference in performance/price ratio, though; I just wanted to make you aware of that.
answered Apr 14 at 14:04
Henrik SchumacherHenrik Schumacher
63.2k587176
$endgroup$
$begingroup$
Might you elaborate on what make Mma'sParallelframeowrk "almost useless" in your view?
$endgroup$
– Alan
Apr 14 at 15:09
$begingroup$
I do use the parallel tools a lot. I would not call them useless at all. It is true that parallelizing tight inner loops will often not provide much speedup (sometimes it will though), but parallelization is quite useful for running a slow function many times (e.g. batch processing).
$endgroup$
– Szabolcs
Apr 14 at 15:15
1
$begingroup$
@Alan Okay, I admit, that was a bit opinionated. I rephrased the statement and also detailed out my argumentation.
$endgroup$
– Henrik Schumacher
Apr 14 at 15:46
1
$begingroup$
"I have the tendency not to use slow functions". What I meant was that a loop (Table, Map) is worth parallelizing if each iteration takes a noticeable time. In these cases, it can provide a significant speedup. If each iteration takes 10^-6 seconds, then it won't. If you have a big enough problem, then it will always take a while to run. People run highly optimized C programs for days on HPC clusters :-)
$endgroup$
– Szabolcs
Apr 14 at 16:28
2
$begingroup$
E.g. last time I needed to do some image processing. Just a PeronaMalikFilter will often take a noticeable amount of time. I had hundreds of images. Parallelization was a huge help. But only the outermost loop was parallelized (i.e. batch-process all images).
$endgroup$
– Szabolcs
Apr 14 at 16:30
|
show 1 more comment
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1 Answer
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1 Answer
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$begingroup$
It is right, you can run only two Mathematica kernels at once in the Home Edition. But whether this is an actual limitation (and whether you should prefer a CPU with high single-core throughput) depends on what you are about to do with Mathematica.
I can only speak for my personal scope of application which is numerics. Here I seldomly find the Parallel-framework helpful, as Compiled code with options RuntimeAttributes -> Listable and Parallelization -> True usually scales much better than Parallel and friends. Such parallelized code is sped up by Intel MKL (and OpenMP?) outside of the Mathematica kernel. Even better is to use vectorized code for linear algebra; such is accelerated also by Intel MKL and such low level libraries as BLAS. The hardware ressources used are also independent of the number of Mathematica kernels.
So, for numerical computations, you may enjoy mostly all the benefits of a fat multi core CPU, even with the Home Edition.
Things might be different for vast symbolic computations, though, or when you are forced to run slow built-in functions that are not parallelized themselves and that you cannot reimplement yourselves more efficiently.
If you are interested in neural network facilities, you might prefer NVidia graphics cards over those by AMD since only CUDA, not OpenCL is supported. Anyways, most algorithms in Mathematica are not optimized for GPUs, so apart from the very localized application of neural networks, also the particular choice of GPU does not matter much.
In think the only general advice that one can give is: Having lots of memory is always a good idea and does not harm in any case. Apart from that, I doubt that it is a good idea to build your new machine depending on your Mathematica license.
Addendum
A propos Intel MKL: I really hate to say this (and it might cost me further downvotes), but as far as numerics in Mathematica are considered, one should keep in mind that Intel CPUs might be preferable over AMD CPUs just because Intel MKL is optimized for Intel hardware (and rumor has it that AMD processors are nerfed in MKL). I cannot quantify the difference in performance/price ratio, though; I just wanted to make you aware of that.
answered Apr 14 at 14:04
Henrik SchumacherHenrik Schumacher
63.2k587176
$endgroup$
$begingroup$
Might you elaborate on what make Mma'sParallelframeowrk "almost useless" in your view?
$endgroup$
– Alan
Apr 14 at 15:09
$begingroup$
I do use the parallel tools a lot. I would not call them useless at all. It is true that parallelizing tight inner loops will often not provide much speedup (sometimes it will though), but parallelization is quite useful for running a slow function many times (e.g. batch processing).
$endgroup$
– Szabolcs
Apr 14 at 15:15
1
$begingroup$
@Alan Okay, I admit, that was a bit opinionated. I rephrased the statement and also detailed out my argumentation.
$endgroup$
– Henrik Schumacher
Apr 14 at 15:46
1
$begingroup$
"I have the tendency not to use slow functions". What I meant was that a loop (Table, Map) is worth parallelizing if each iteration takes a noticeable time. In these cases, it can provide a significant speedup. If each iteration takes 10^-6 seconds, then it won't. If you have a big enough problem, then it will always take a while to run. People run highly optimized C programs for days on HPC clusters :-)
$endgroup$
– Szabolcs
Apr 14 at 16:28
2
$begingroup$
E.g. last time I needed to do some image processing. Just a PeronaMalikFilter will often take a noticeable amount of time. I had hundreds of images. Parallelization was a huge help. But only the outermost loop was parallelized (i.e. batch-process all images).
$endgroup$
– Szabolcs
Apr 14 at 16:30
|
show 1 more comment
$begingroup$
It is right, you can run only two Mathematica kernels at once in the Home Edition. But whether this is an actual limitation (and whether you should prefer a CPU with high single-core throughput) depends on what you are about to do with Mathematica.
I can only speak for my personal scope of application which is numerics. Here I seldomly find the Parallel-framework helpful, as Compiled code with options RuntimeAttributes -> Listable and Parallelization -> True usually scales much better than Parallel and friends. Such parallelized code is sped up by Intel MKL (and OpenMP?) outside of the Mathematica kernel. Even better is to use vectorized code for linear algebra; such is accelerated also by Intel MKL and such low level libraries as BLAS. The hardware ressources used are also independent of the number of Mathematica kernels.
So, for numerical computations, you may enjoy mostly all the benefits of a fat multi core CPU, even with the Home Edition.
Things might be different for vast symbolic computations, though, or when you are forced to run slow built-in functions that are not parallelized themselves and that you cannot reimplement yourselves more efficiently.
If you are interested in neural network facilities, you might prefer NVidia graphics cards over those by AMD since only CUDA, not OpenCL is supported. Anyways, most algorithms in Mathematica are not optimized for GPUs, so apart from the very localized application of neural networks, also the particular choice of GPU does not matter much.
In think the only general advice that one can give is: Having lots of memory is always a good idea and does not harm in any case. Apart from that, I doubt that it is a good idea to build your new machine depending on your Mathematica license.
Addendum
A propos Intel MKL: I really hate to say this (and it might cost me further downvotes), but as far as numerics in Mathematica are considered, one should keep in mind that Intel CPUs might be preferable over AMD CPUs just because Intel MKL is optimized for Intel hardware (and rumor has it that AMD processors are nerfed in MKL). I cannot quantify the difference in performance/price ratio, though; I just wanted to make you aware of that.
answered Apr 14 at 14:04
Henrik SchumacherHenrik Schumacher
63.2k587176
$endgroup$
$begingroup$
Might you elaborate on what make Mma'sParallelframeowrk "almost useless" in your view?
$endgroup$
– Alan
Apr 14 at 15:09
$begingroup$
I do use the parallel tools a lot. I would not call them useless at all. It is true that parallelizing tight inner loops will often not provide much speedup (sometimes it will though), but parallelization is quite useful for running a slow function many times (e.g. batch processing).
$endgroup$
– Szabolcs
Apr 14 at 15:15
1
$begingroup$
@Alan Okay, I admit, that was a bit opinionated. I rephrased the statement and also detailed out my argumentation.
$endgroup$
– Henrik Schumacher
Apr 14 at 15:46
1
$begingroup$
"I have the tendency not to use slow functions". What I meant was that a loop (Table, Map) is worth parallelizing if each iteration takes a noticeable time. In these cases, it can provide a significant speedup. If each iteration takes 10^-6 seconds, then it won't. If you have a big enough problem, then it will always take a while to run. People run highly optimized C programs for days on HPC clusters :-)
$endgroup$
– Szabolcs
Apr 14 at 16:28
2
$begingroup$
E.g. last time I needed to do some image processing. Just a PeronaMalikFilter will often take a noticeable amount of time. I had hundreds of images. Parallelization was a huge help. But only the outermost loop was parallelized (i.e. batch-process all images).
$endgroup$
– Szabolcs
Apr 14 at 16:30
|
show 1 more comment
$begingroup$
It is right, you can run only two Mathematica kernels at once in the Home Edition. But whether this is an actual limitation (and whether you should prefer a CPU with high single-core throughput) depends on what you are about to do with Mathematica.
I can only speak for my personal scope of application which is numerics. Here I seldomly find the Parallel-framework helpful, as Compiled code with options RuntimeAttributes -> Listable and Parallelization -> True usually scales much better than Parallel and friends. Such parallelized code is sped up by Intel MKL (and OpenMP?) outside of the Mathematica kernel. Even better is to use vectorized code for linear algebra; such is accelerated also by Intel MKL and such low level libraries as BLAS. The hardware ressources used are also independent of the number of Mathematica kernels.
So, for numerical computations, you may enjoy mostly all the benefits of a fat multi core CPU, even with the Home Edition.
Things might be different for vast symbolic computations, though, or when you are forced to run slow built-in functions that are not parallelized themselves and that you cannot reimplement yourselves more efficiently.
If you are interested in neural network facilities, you might prefer NVidia graphics cards over those by AMD since only CUDA, not OpenCL is supported. Anyways, most algorithms in Mathematica are not optimized for GPUs, so apart from the very localized application of neural networks, also the particular choice of GPU does not matter much.
In think the only general advice that one can give is: Having lots of memory is always a good idea and does not harm in any case. Apart from that, I doubt that it is a good idea to build your new machine depending on your Mathematica license.
Addendum
A propos Intel MKL: I really hate to say this (and it might cost me further downvotes), but as far as numerics in Mathematica are considered, one should keep in mind that Intel CPUs might be preferable over AMD CPUs just because Intel MKL is optimized for Intel hardware (and rumor has it that AMD processors are nerfed in MKL). I cannot quantify the difference in performance/price ratio, though; I just wanted to make you aware of that.
answered Apr 14 at 14:04
Henrik SchumacherHenrik Schumacher
63.2k587176
$endgroup$
It is right, you can run only two Mathematica kernels at once in the Home Edition. But whether this is an actual limitation (and whether you should prefer a CPU with high single-core throughput) depends on what you are about to do with Mathematica.
I can only speak for my personal scope of application which is numerics. Here I seldomly find the Parallel-framework helpful, as Compiled code with options RuntimeAttributes -> Listable and Parallelization -> True usually scales much better than Parallel and friends. Such parallelized code is sped up by Intel MKL (and OpenMP?) outside of the Mathematica kernel. Even better is to use vectorized code for linear algebra; such is accelerated also by Intel MKL and such low level libraries as BLAS. The hardware ressources used are also independent of the number of Mathematica kernels.
So, for numerical computations, you may enjoy mostly all the benefits of a fat multi core CPU, even with the Home Edition.
Things might be different for vast symbolic computations, though, or when you are forced to run slow built-in functions that are not parallelized themselves and that you cannot reimplement yourselves more efficiently.
If you are interested in neural network facilities, you might prefer NVidia graphics cards over those by AMD since only CUDA, not OpenCL is supported. Anyways, most algorithms in Mathematica are not optimized for GPUs, so apart from the very localized application of neural networks, also the particular choice of GPU does not matter much.
In think the only general advice that one can give is: Having lots of memory is always a good idea and does not harm in any case. Apart from that, I doubt that it is a good idea to build your new machine depending on your Mathematica license.
Addendum
A propos Intel MKL: I really hate to say this (and it might cost me further downvotes), but as far as numerics in Mathematica are considered, one should keep in mind that Intel CPUs might be preferable over AMD CPUs just because Intel MKL is optimized for Intel hardware (and rumor has it that AMD processors are nerfed in MKL). I cannot quantify the difference in performance/price ratio, though; I just wanted to make you aware of that.
answered Apr 14 at 14:04
Henrik SchumacherHenrik Schumacher
63.2k587176
edited Apr 14 at 17:21
answered Apr 14 at 14:04
Henrik SchumacherHenrik Schumacher
63.2k587176
answered Apr 14 at 14:04
Henrik SchumacherHenrik Schumacher
63.2k587176
answered Apr 14 at 14:04
Henrik SchumacherHenrik Schumacher
63.2k587176
63.2k587176
$begingroup$
Might you elaborate on what make Mma'sParallelframeowrk "almost useless" in your view?
$endgroup$
– Alan
Apr 14 at 15:09
$begingroup$
I do use the parallel tools a lot. I would not call them useless at all. It is true that parallelizing tight inner loops will often not provide much speedup (sometimes it will though), but parallelization is quite useful for running a slow function many times (e.g. batch processing).
$endgroup$
– Szabolcs
Apr 14 at 15:15
1
$begingroup$
@Alan Okay, I admit, that was a bit opinionated. I rephrased the statement and also detailed out my argumentation.
$endgroup$
– Henrik Schumacher
Apr 14 at 15:46
1
$begingroup$
"I have the tendency not to use slow functions". What I meant was that a loop (Table, Map) is worth parallelizing if each iteration takes a noticeable time. In these cases, it can provide a significant speedup. If each iteration takes 10^-6 seconds, then it won't. If you have a big enough problem, then it will always take a while to run. People run highly optimized C programs for days on HPC clusters :-)
$endgroup$
– Szabolcs
Apr 14 at 16:28
2
$begingroup$
E.g. last time I needed to do some image processing. Just a PeronaMalikFilter will often take a noticeable amount of time. I had hundreds of images. Parallelization was a huge help. But only the outermost loop was parallelized (i.e. batch-process all images).
$endgroup$
– Szabolcs
Apr 14 at 16:30
|
show 1 more comment
$begingroup$
Might you elaborate on what make Mma'sParallelframeowrk "almost useless" in your view?
$endgroup$
– Alan
Apr 14 at 15:09
$begingroup$
I do use the parallel tools a lot. I would not call them useless at all. It is true that parallelizing tight inner loops will often not provide much speedup (sometimes it will though), but parallelization is quite useful for running a slow function many times (e.g. batch processing).
$endgroup$
– Szabolcs
Apr 14 at 15:15
1
$begingroup$
@Alan Okay, I admit, that was a bit opinionated. I rephrased the statement and also detailed out my argumentation.
$endgroup$
– Henrik Schumacher
Apr 14 at 15:46
1
$begingroup$
"I have the tendency not to use slow functions". What I meant was that a loop (Table, Map) is worth parallelizing if each iteration takes a noticeable time. In these cases, it can provide a significant speedup. If each iteration takes 10^-6 seconds, then it won't. If you have a big enough problem, then it will always take a while to run. People run highly optimized C programs for days on HPC clusters :-)
$endgroup$
– Szabolcs
Apr 14 at 16:28
2
$begingroup$
E.g. last time I needed to do some image processing. Just a PeronaMalikFilter will often take a noticeable amount of time. I had hundreds of images. Parallelization was a huge help. But only the outermost loop was parallelized (i.e. batch-process all images).
$endgroup$
– Szabolcs
Apr 14 at 16:30
$begingroup$
Might you elaborate on what make Mma's
Parallel frameowrk "almost useless" in your view?$endgroup$
– Alan
Apr 14 at 15:09
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Might you elaborate on what make Mma's
Parallel frameowrk "almost useless" in your view?$endgroup$
– Alan
Apr 14 at 15:09
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I do use the parallel tools a lot. I would not call them useless at all. It is true that parallelizing tight inner loops will often not provide much speedup (sometimes it will though), but parallelization is quite useful for running a slow function many times (e.g. batch processing).
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– Szabolcs
Apr 14 at 15:15
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I do use the parallel tools a lot. I would not call them useless at all. It is true that parallelizing tight inner loops will often not provide much speedup (sometimes it will though), but parallelization is quite useful for running a slow function many times (e.g. batch processing).
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– Szabolcs
Apr 14 at 15:15
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1
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@Alan Okay, I admit, that was a bit opinionated. I rephrased the statement and also detailed out my argumentation.
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– Henrik Schumacher
Apr 14 at 15:46
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@Alan Okay, I admit, that was a bit opinionated. I rephrased the statement and also detailed out my argumentation.
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– Henrik Schumacher
Apr 14 at 15:46
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1
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"I have the tendency not to use slow functions". What I meant was that a loop (Table, Map) is worth parallelizing if each iteration takes a noticeable time. In these cases, it can provide a significant speedup. If each iteration takes 10^-6 seconds, then it won't. If you have a big enough problem, then it will always take a while to run. People run highly optimized C programs for days on HPC clusters :-)
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– Szabolcs
Apr 14 at 16:28
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"I have the tendency not to use slow functions". What I meant was that a loop (Table, Map) is worth parallelizing if each iteration takes a noticeable time. In these cases, it can provide a significant speedup. If each iteration takes 10^-6 seconds, then it won't. If you have a big enough problem, then it will always take a while to run. People run highly optimized C programs for days on HPC clusters :-)
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– Szabolcs
Apr 14 at 16:28
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2
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E.g. last time I needed to do some image processing. Just a PeronaMalikFilter will often take a noticeable amount of time. I had hundreds of images. Parallelization was a huge help. But only the outermost loop was parallelized (i.e. batch-process all images).
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– Szabolcs
Apr 14 at 16:30
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E.g. last time I needed to do some image processing. Just a PeronaMalikFilter will often take a noticeable amount of time. I had hundreds of images. Parallelization was a huge help. But only the outermost loop was parallelized (i.e. batch-process all images).
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– Szabolcs
Apr 14 at 16:30
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$begingroup$
Related: Best Mac for Mathematica and Which processor and graphics card to use for my Mathematica license
$endgroup$
– C. E.
Apr 14 at 19:13
1
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Common recommendations are to get a lot of RAM and SSD.
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– C. E.
Apr 14 at 19:15
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I agree with comment above! RAM and drive size is a definite consideration issue. I recently purchased a new MacBook Pro 15" with 16 MB RAM and 1 TB SSD. I regularly perform analysis on time based data files that are hundreds of mega bytes in size. This MacBook Pro is better than my work desktop system. The biggest issue for me is RAM. I need to get a lot data in memory where I can perform the Mathematica operations. I say go with as much RAM, drive, and processor as your wallet will allow. Mathematica runs great on MacOS.
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– Joseph
Apr 14 at 19:35