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...st.ll -S -o -" . But the output shows that there is code motion around the barrier intrinsics. test.ll ------- ; ModuleID = 'test.bc' define void @test(i16* %I_0, i16* %I_1, i16* %I_2, i16* %I_3, i16* %O_0) { entry: %T_0 = load volatile i16* %I_0 %T_1 = load volatile i16* %I_1 %T_2 = load volatile i16* %I_2 %T_3 = load volatile i16* %I_3 call void @llvm.nvvm.barrier0() %T_5 = add i16 %T_1, %T_3 call void @llvm.nvvm.barrier0() %T_7 = mul i16 %T_0, %T_2 %T_8 = xor i16 %T_2, %T_0 %T_9 = mul i16 %T_0, %T_1 call void @llvm.nvvm.barrier0() %T_11 = sub i16 %T_7, %T...

is there any guarantee that the nvptx intrinsic "llvm.nvvm.barrier0" will not be moved around by opt ? In other words, can I expect all the instructions above "llvm.nvvm.barrier0" to remain above it and those below it to remain below, after all the opt passes are run ? If that is not the case, is there a way to define such an intrinsic ? Thanks. -------------- next part

Hello, can anybody here explain what a Jonckheere-Terpstra test is and whether it is implemented in R? I just know it's a non-parametric test, otherwise I've no clue about it ;-( . Are there alternatives to this test? thanks for help, Arne

...+ x_2[, 1] + x_1[, 2] + x_2[, 2]   Where   x_1[, i] = cos(2 * pi * t / T_i) x_2[, i] = sin(2 * pi * t / T_i)   i = 1, 2   Data have two columns: t and y.   As you can see, I have a multiple components model, with rithm and without trends, and I have a fundamental period (T_1 = 24 hour; T_2 = 12 hour).   I have to compare the parameters between the two models (one for each subject), using a parametric test as described in the doc I adjunt (page 500, Parametric solution):   I have to reach results as follow:   ______________________________________________________ H0: Equality o...

I'm trying to use the rsync algorithm for incremental backups. After a quick look at rsync I saw the batch mode operations, and I thought that maybe I can modify them for incremental backups. What is needed is to add an option to save the checksums of all the files of the level 0 backup and a second option to use the level n checksum to calculate the delta batch files for the level n+1

...usually 3 to 5, whereas N is of the order of 1000). Every object has its own label L_i, i=1...N, that is known. For each of these objects I measure some property in time (let's say I measure it Q times in a given time interval), i.e. the i-th object has an associated file {t, y}, where t=(t_1,t_2....t_Q) and y=(y_1,y_2,...y_Q). My problem is then to come up with an algorithm that after learning on the training dataset, can guess the labels of a testing dataset. The difference with respect to the datamining I have done so far is that I do not have a set of properties for every object (e.g....

...to attr_accessor dynamically in controller. And am sending the data object to display in Yaml format. I am getting the response along with changed_attributes but I don''t want changed_attributes details in my response. build Model code class Test < ActiveRecord::Base attr_accessor: t_1, t_2, t_3, t_4 end In Test Controller code: #build is controller object tb = ["[5, 30.14]","[0, 12.0]","[1, 11.0]","[2, 11.0]"] $i=1 while $i <= tb.length do $tbVal = "t_#{$i}" build["#{$tbVal}"] = tb[$i-1] $i += 1 wants.yaml { render :te...