similar to: [LLVMdev] Power/Energy Awareness in LLVM

Thank you for the link! It is not clear though what -Oe actually implements. Does it simply optimize for speed as suggested by Chris or it does other things? Generally speaking, speed correlates well with low energy, because completing the task in fewer cycles means consuming less energy unless the speed optimization causes the energy per cycle to be significantly higher. In theory, some compiler

Hi All, Does LLVM currently have any power/energy reduction optimizations or any performance optimizations that take power/energy consumption into account? We are currently working on a research project on instruction scheduling for low power (experimenting with different algorithms for minimizing switching power) and would like to find out if anyone in the LLVM community is interested in adding

See http://llvm.org/bugs/show_bug.cgi?id=6210. -- Sean Silva -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20130415/e948b489/attachment.html>

On Sep 13, 2011, at 4:01 AM, Kyriakos Georgiou wrote: > I've been investigating a case with the XCore target (which doesn't use > FastISel) where the DWARF line number emitted at -O0 results in the xgdb > visiting source lines in an unexpected order. I've tracked down the > problem to the handling of DebugLocs in the selection DAG, in the getNode > method shown bellow.

On 13/09/11 17:40, Devang Patel wrote: > On Sep 13, 2011, at 4:01 AM, Kyriakos Georgiou wrote: >> I've been investigating a case with the XCore target (which doesn't use >> FastISel) where the DWARF line number emitted at -O0 results in the xgdb >> visiting source lines in an unexpected order. I've tracked down the >> problem to the handling of DebugLocs in

On 15 Apr 2013, at 16:03, Sean Silva <silvas at purdue.edu> wrote: > See http://llvm.org/bugs/show_bug.cgi?id=6210. Chris is correct at the coarse granularity, but there are some trades to be made at the fine. There is some interesting work from MIT in the context of image processing kernels related to the relative costs of saving intermediates out to cache or DRAM vs recomputing them

Hi, I want to remove metadata from an instruction. Is there any way doing this in my pass? Regards, Kyriakos -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20130815/08200e23/attachment.html>

Preallocation Instruction Scheduling with Register Pressure Minimization Using a Combinatorial Optimization Approach G. Shobaki, M. Shawabkeh and N. Abu-Rmaileh ACM Transactions on Architecture and Code Optimization (TACO). vol. 10, issue 3, Article 14 (Sept. 2013) http://dx.doi.org/10.1145/2512432 Regards Ghassan Shobaki, PH.D Assistant Professor Department of Computer Science Princess Sumaya

Hola, Otra forma, utilizando la función de intervalos y la que comprueba si otro intervalo se solapa del paquete "lubridate": #---------------------- library(lubridate) fe.chas <- data.frame( entra=c('2001-01-01','2001-06-01','2003-01-01') ,sale=c('2002-01-01','2002-06-01','2004-01-01') ) ref <-

I've been investigating a case with the XCore target (which doesn't use FastISel) where the DWARF line number emitted at -O0 results in the xgdb visiting source lines in an unexpected order. I've tracked down the problem to the handling of DebugLocs in the selection DAG, in the getNode method shown bellow. It first tries to find if a similar node already exists in order to use that

Dear list members, I have a statistical question, that doesn't belong to this list, and I apologise for that in advance but I would appreciate your help very much. Is there some convention for selecting the a level for significance testing in scientific (e.g. chemical processes) studies? Most people use the 0.05 level but I could not find a reference to justify this. Why not 0.01 or 0.1?

Please, forgive my ignorance on statistics, but I have a rather simple question concerning AIC. Small values of AIC mean good fit of the model. How about negative values of AIC? Is a model with AIC=0.5 considered to have better fit than a model with AIC=-500? I couldn't find anything explaining this in the documentation or any elementary statistics textbook that I have at my disposal. Thanks

On Jul 2, 2013, at 2:35 PM, Ghassan Shobaki <ghassan_shobaki at yahoo.com> wrote: > Thank you for the answers! We are currently trying to test the MI scheduler. We are using LLVM 3.3 with Dragon Egg 3.3 on an x86-64 machine. So far, we have run one SPEC CPU2006 test with the MI scheduler enabled using the option -fplugin-arg-dragonegg-llvm-option='-enable-misched:true' with -O3.

When I try to run the "sammon" function of the multiv package, I always get this error message: Error in as.vector(dist(a)) : couldn't find function "dist" It happens even with example(sammon). I am running R 1.5.0 on Win98 and I have still installed R 1.4.1 but it doesn't work on the old R version with the older multiv package either. Is there a problem with the

Dear list members, this is not an R question and forgive me for using the list for irrelevant questions, but this is the only place I know where I can find some good statisticians and I need an expert opinion. There is this power law kinetic model of the form: M=kt^n where t is the time, M is the fraction of drug released, k is the rate constant and n is an exponent related to the mechanism of

Thank you for the answers, Jakob! That's really informative for someone who is still new to LLVM like me. Please see my responses below. -Ghassan  ________________________________ From: Jakob Stoklund Olesen <stoklund at 2pi.dk> To: Ghassan Shobaki <ghassan_shobaki at yahoo.com> Cc: "llvmdev at cs.uiuc.edu" <llvmdev at cs.uiuc.edu> Sent: Tuesday, August 16,

Hi all, I have a question regarding ACD for queues. What happens when I have 2 or more queues with same weight and each queue has a call in? How will it decide which call will be routed to the next available agent? Will it take the call with the longest waiting time in queue? If not how would I do this? Also can someone point me to resources for making a single queue with customer calls

Hello list members, I am new to this list and don't know very much about what R can do or how to program it, but I am interested in plotting 4 variables as sequential contour plots (or surface plots), something like the conditioning plots for 4 variables (coplot(X1~X2|X3+X4)). To be more specific, I need a tool that will automatically create contour (orsurface) plots of X1 vs X2 vs X3 for

Thank you for the answers! We are currently trying to test the MI scheduler. We are using LLVM 3.3 with Dragon Egg 3.3 on an x86-64 machine. So far, we have run one SPEC CPU2006 test with the MI scheduler enabled using the option -fplugin-arg-dragonegg-llvm-option='-enable-misched:true' with -O3. This enables the machine scheduler in addition to the SD scheduler. We have verified this by

I should note here that although SPEC provided us with a sufficiently large sample for our spill-count experiment, I don't think that SPEC has enough hot functions with spills to make our execution-time results statistically significant. That's because SPEC has many benchmarks with peaky profiles, where one of two functions dominate the execution time. So, if one heuristic gets very