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49

It's a bad idea because vector needs to allocate as many objects in space as there are rows in your matrix. Allocation is expensive, but primarily it is a bad idea because the data of your matrix now exists in a number of arrays scattered around memory, rather than all in one place where the processor cache can easily access it. It's also a wasteful storage ...


37

I'll try to summarize my experiences obtained in the course of developing ViennaCL, where we have CUDA and OpenCL backends with mostly 1:1 translations of a lot of compute kernels. From your question I'll also assume that we are mostly taking about GPUs here. Performance Portability. First of all, there is no such thing as performance-portable kernels in ...


21

In addition to the reasons Wolfgang mentioned, if you use a vector<vector<double> >, you'll have to dereference it twice every time you want to retrieve an element, which is more computationally costly than a single dereferencing operation. One typical approach is to allocate a single array (a vector<double> or a double *) instead. I've ...


19

Ultimately, the answer to this question depends on the prices being charged for the services that you need. At some very low price, this would almost certainly be better than buying your own computer, while at some higher price you would be better off buying your own computer. The case for using a shared resource is pretty strong though and these factors ...


17

Double precision is fairly common on newer GPUs. For instance I own a NVIDIA GTX560 Ti (fairly low end when it comes to computing) that has no issue running ViennaCL in double precision. From here (section 4) it appears all NVIDIA cards from GTX4xx onward support double precision natively. I would guess that the GROMACS information is simply outdated.


16

We are currently writing a paper that contains a number of comparable plots, and we more or less had the same problem. The paper is about comparing the scaling of different algorithms over the number of cores, which ranges between 1 and up to 100k on a BlueGene. The reason for using loglog-plots in this situation is the number of orders of magnitude involved....


15

This issue has become much more nuanced as the changes in architectures has shifted the HPC landscape. As Wolfgang Bangerth mentions one current longstanding view, I'll split my answer into basic definitiions and further details. Basic Definition A node refers to the physical box, i.e. cpu sockets with north/south switches connecting memory systems and ...


14

Georg Hager wrote about this in Fooling the Masses - Stunt 3: The log scale is your friend. While it is true that log-log plots of strong scaling are not very discerning on the high end, they allow for showing scaling across many more orders of magnitude. To see why this is useful, consider a 3D problem with regular refinement. On a linear scale, you can ...


13

BLAS1-operations, BLAS2-operations, and sparse-operations share the same curse of low arithmetic intensity, that they perform $O(1)$ flops for each memory read (contrast this to a BLAS3-operation like gemm, which performs $O(N^3)$ flops over $O(N^2)$ reads and only becomes more and more arithmetic-intensive/compute-bound for large $N$). However, sparse ...


12

$6.60/core-month is less than a penny a core-hour. This is a good deal, and it's a better deal than you can get if you buy identical hardware yourself and pay your own power and sysadmin bill. If all you are going to do is buy one probably less powerful workstation node with sufficient RAM, then you may do better than this, but you may also complete your ...


11

For such a small simulation, I would strongly suggest looking into GPU-based solutions. This is probably what will get you the most ns/day/Euro. In my opinion, the fastest fully-featured GPU-based Molecular Dynamics (MD) software out there is ACEMD (see here for timings). The software, however, is commercial, but has a single-GPU free version that can be ...


11

I would say that there are a number of reasons why there are no computational science contests besides the potentially massive computational resources required. Time limits: Writing scientific computing code is usually not something that you want to rush. A lot of emphasis is on making sure it is correct, and thorough consideration of test/corner cases. ...


11

Both the standard cluster and custom supercomputer (Anton) versions of molecular dynamics at D. E. Shaw Research are both deterministic and parallel invariant. That is, a test run on a single core generates the same bits as a massively parallel run. The techniques include Integer summation: Although each force term is computed in floating point, the total ...


10

No, use one of the free available linear algebra libraries. A discussion about different libraries can be found here: Recommendations for a usable, fast C++ matrix library?


10

A long standing favorite benchmark in high performance computing has been the HPLinpack benchmark, which measures the speed of a computer system in floating point operations per second while solving a very large, dense, linear system of equations. It is assumed that the solution takes $2/3n^{3}+2n^{2}$ floating point operations and the tester is allowed to ...


8

deal.II uses the Threading Building Blocks throughout the library and by and large we're reasonably happy with it. We've looked at a few alternatives, in particular OpenMP since everyone seems to be using that for simpler codes, but found them lacking. In particular, OpenMP has the huge disadvantage that its task model does not allow you to get a handle for ...


8

Many of us in scientific computing simply have well-equipped laptops for regular software development tasks, some multicore workstations for smaller-scale testing, and access to clusters for larger runs. To give you an idea: My laptop is a Dell M3800 (4-core Intel i7, hyperthreading, 16GB of RAM). This is good enough to regularly compile my software and do ...


8

Like Brian said, the Xeon Phi cores are not at all comparable to the CUDA ones. The problem with the Phi is that it's somewhere between two horses. If you are doing highly parallel floating point calculations, NVIDIA will provide you with something like 3 times the performance at 1/4th of the price. For double precision the gap is smaller, but NVIDIA still ...


8

I realize this question was asked a while ago, but I recently needed the Freschet distance as well. I couldn't find any implementations for Python, so I wrote my own based on the paper: "Computing Discrete Frechet Distance" by "Thomas Eiter and Heikki Mannila", and thought I would share it for future reference. It's written in Cython (save as frechet.pyx) #...


7

Almost everything you can build and install in your own space. With GNU autotools, you can do something like ./configure --prefix=/path/to/your/work/space ... and then follow the usual compilation instructions. Things based on CMake and Scons have similar facilities.


7

CUDA cores aren't at all comparable to the separate processor cores in the Xeon Phi coprocessors. The Phi coprocessor cores are full fledged processors that can have their own loops, branching, etc. while the CUDA cores are all executing the same operations on various slices of your data.


7

One of the first things that you need to understand about parallel programming is the difference between shared memory multiprocessor computer systems and distributed memory clusters. A shared memory multiprocessor system is a computer in which several processor cores (which might be on one, two, or more integrated circuits) share the same memory. From ...


6

Many modern computers have multiple CPUs (chips) and each chip may have multiple cores. Each core can execute one (or sometimes multiple) stream of instructions. I've recorded an introduction to these sorts of issues here: http://www.math.tamu.edu/~bangerth/videos.676.39.html


6

If you work at an institution in the US or have collaborators at one, you should look into the XSEDE program. This is a federation of the National Science Foundation funded supercomputing centers in the US which provides compute cycles on a variety of large-scale machines to open science research in the US. There are a couple of machines with GPU resources ...


6

Yes, a number of universities have graduate programs in HPC. Often they're referred to as computational science or scientific computation programs. I'm currently pursuing a PhD in scientific computation through the University of Minnesota, and a Master's degree is also offered. Florida State has a department of scientific computing and a graduate program, ...


6

It will not scale! Use the parallel HDF5 functionality to read it in parallel with a relatively small number of readers, or do what your colleague suggests. There will be tradeoffs with both methods, and you will have to do some tuning. It depends pretty strongly on how big the file is, how many tasks you have reading it, what the underlying filesystem is (...


6

Like WolfgangBangerth, I strongly recommend that you reconsider your motivation (or your supervisor's motivation) for this goal. First, look at Center for Exascale Simulation of Combustion in Turbulence (ExaCT) web site. They have some proxy applications that may be of use to you in testing your mechanisms, and publications by authors such as John Bell, Joe ...


6

Will you be using it all the time, for a long time? In general, the economics for this are simple - if you need a valuable resource for short intermittent bursts, it will generally be cheaper to rent than own; and if you expect to use most of it for a prolonged time then it will be cheaper to own it. A simple rule of thumb actually is about the terms used -...


6

You could install BOINC on those machines. When the computers become idle, the BOINC screensaver/client requests tasks from a server and computes them. See more information about it here. This is the software used by a number of projects such as SETI@Home. You can create your own project with BOINC and then put your desktop machines to work.


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