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I believe this may be a bug in version 10 as in version 9.0.1 the definition does get distributed. What happens in version 10 also directly contradicts the documentation. Here's an illustration:


Your assumptions are correct. Every front end process that is started up will show in the dock. Certain operations, such as rasterization, require a front end. If you rasterize in parallel, each subkernel will start its own front end, which will show up in the dock.


Calling tab = {{x1,y1,z1},{x2,y2,z2},{xn,yn,zn}}; We have ParallelMap[{First[#], f @@ Rest[#]} &, tab]


I would rethink your data format. Consider using "indexed objects" (DownValues) or perhaps Associations. One example: d["apple"] = {1, 2, 3}; s["apple"] = 1; d["banana"] = {10, 20, 30}; s["banana"] = 10; d["kiwi"] = {100, 200, 300}; s["kiwi"] = 100; data = {"apple", "banana", "kiwi"}; myfun[data_, scale_] := Total[data]/scale ...


tab = Table[{x[n], y[n], z[n]}, {n, 4}] {{x[1], y[1], z[1]}, {x[2], y[2], z[2]}, {x[3], y[3], z[3]}, {x[4], y[4], z[4]}} ParallelMap[{#, f[##2]} & @@ # &, tab] {{x[1], f[y[1], z[1]]}, {x[2], f[y[2], z[2]]}, {x[3], f[y[3], z[3]]}, {x[4], f[y[4], z[4]]}} See Apply and SlotSequence for clarification.


While I am not very familiar with CUDA, this answer on StackOverflow suggests that this is not possible. Quoting the relevant parts: Q: Is it possible to have two or more linux host processes that can access the same device memory? -- Mark Borgerding A: My understanding of the CUDA APIs is that this cannot be done. The device pointers are relative ...


What about testing with good and (purposely) bad implementations of number theory functions? The bad implementation tests your cluster, the good gives you a fast, modifiable answer. For example, PowerMod is faster than Mod. ParallelSum[Mod[3749111187234987^n, 743], {n, 1, 10000}]] ParallelSum[PowerMod[3749111187234987, n, 743], {n, 1, 10000}] but becomes ...

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