cortex | Production infrastructure for machine learning at scale | AWS library

Goal

I'm building a mutex primitive using gcc inline assembly for a CortexM7 target using the LDREX and STREX instructions, following the Barrier Litmus Tests and Cookbook document from ARM.

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I am trying to link Cortex-M4 firmware with clang + lld. The gcc build works fine. I am using the stock CMSIS linker script with only RAM & ROM size adjusted (bases are the same). Beginning of the script (without comments):

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I am using a STM32 MCU with arm cortex m4 and want to use the gsl-2.7.1. However, I already tried for example the command ./configure --prefix=/home/user_name/gsl_arm --target=arm-none-eabi and every other suggestion that I could find on the internet and toolchain-tutorials, but in the best case I got during linking with the build library an error like "could not recognize the symbols". In the worst case, the suggested options for autoconfig were not recognized (for example, to specify the cpu). Does anyone have an idea how I have to crosscompile it?

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I have two samples from the population of neurons in the brain, each sample consisting of a thousand neuron instances, of categories:

1. cerebellum
2. cortex

Now I'm extracting multiple metrics for each sample using complex network analysis, for example, neuron degree of connectivity k, a discreet number n = 0, 1, ...., n, or clustering coefficient C, a continous value between 0.00000 and 1.00000.

df.sample(3) (where web is category) in my pandas dataframes:

cortex:

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In my adventures of experimenting around with the 64-bit ARM architecture, I noticed a peculiar speed difference depending on whether br or ret is used to return from a subroutine.

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I am benchmarking an ARMv7 NEON code on two ARMv8 processors in AArch32 mode: the Cortex-A53 and Cortex-A72. I am using the Raspberry Pi 3B and Raspberry Pi 4B boards with 32-bit Raspbian Buster.

My benchmarking method is as follows:

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I have made a custom external flash loader(.stldr) file for my STM32 based board and this file works great with ST Link Utility(Read, Write and Erase) are work fine and i can Program the board correctly. But when i try to use the created .stldr file by STM32CubeIDE the erasing process well done but when the downloading process gets start then "failed to download Segment[0]" error pups up.

Could any one help me with the problem?

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I want to build an Android app which will be an interface to convert C++ into assembly code for ARM Cortex M3 architecture.

I'm not an android java developer, and I do mainly arduino projects with C/C++. So I need your help to point me in good directions about how to build an android app with java in Android Studio or similar, which will be able to convert from C++ source code to ASM code M3 Cortex.

I did some research and found that I need to use ARM NONE EABI GCC compiler to generate ASM code from C++, simple like these command line instructions:

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Consider the following code, running on an ARM Cortex-A72 processor (optimization guide here). I have included what I expect are resource pressures for each execution port:

Instruction B I0 I1 M L S F0 F1 .LBB0_1: ldr q3, [x1], #16 0.5 0.5 1 ldr q4, [x2], #16 0.5 0.5 1 add x8, x8, #4 0.5 0.5 cmp x8, #508 0.5 0.5 mul v5.4s, v3.4s, v4.4s 2 mul v5.4s, v5.4s, v0.4s 2 smull v6.2d, v5.2s, v1.2s 1 smull2 v5.2d, v5.4s, v2.4s 1 smlal v6.2d, v3.2s, v4.2s 1 smlal2 v5.2d, v3.4s, v4.4s 1 uzp2 v3.4s, v6.4s, v5.4s 1 str q3, [x0], #16 0.5 0.5 1 b.lo .LBB0_1 1 Total port pressure 1 2.5 2.5 0 2 1 8 1

Although uzp2 could run on either the F0 or F1 ports, I chose to attribute it entirely to F1 due to high pressure on F0 and zero pressure on F1 other than this instruction.

There are no dependencies between loop iterations, other than the loop counter and array pointers; and these should be resolved very quickly, compared to the time taken for the rest of the loop body.

Thus, my intuition is that this code should be throughput limited, and considering the worst pressure is on F0, run in 8 cycles per iteration (unless it hits a decoding bottleneck or cache misses). The latter is unlikely given the streaming access pattern, and the fact that arrays comfortably fit in L1 cache. As for the former, considering the constraints listed on section 4.1 of the optimization manual, I project that the loop body is decodable in only 8 cycles.

Yet microbenchmarking indicates that each iteration of the loop body takes 12.5 cycles on average. If no other plausible explanation exists, I may edit the question including further details about how I benchmarked this code, but I'm fairly certain the difference can't be attributed to benchmarking artifacts alone. Also, I have tried to increase the number of iterations to see if performance improved towards an asymptotic limit due to startup/cool-down effects, but it appears to have done so already for the selected value of 128 iterations displayed above.

Manually unrolling the loop to include two calculations per iteration decreased performance to 13 cycles; however, note that this would also duplicate the number of load and store instructions. Interestingly, if the doubled loads and stores are instead replaced by single LD1/ST1 instructions (two-register format) (e.g. ld1 { v3.4s, v4.4s }, [x1], #32) then performance improves to 11.75 cycles per iteration. Further unrolling the loop to four calculations per iteration, while using the four-register format of LD1/ST1, improves performance to 11.25 cycles per iteration.

In spite of the improvements, the performance is still far away from the 8 cycles per iteration that I expected from looking at resource pressures alone. Even if the CPU made a bad scheduling call and issued uzp2 to F0, revising the resource pressure table would indicate 9 cycles per iteration, still far from actual measurements. So, what's causing this code to run so much slower than expected? What kind of effects am I missing in my analysis?

EDIT: As promised, some more benchmarking details. I run the loop 3 times for warmup, 10 times for say n = 512, and then 10 times for n = 256. I take the minimum cycle count for the n = 512 runs and subtract from the minimum for n = 256. The difference should give me how many cycles it takes to run for n = 256, while canceling out the fixed setup cost (code not shown). In addition, this should ensure all data is in the L1 I and D cache. Measurements are taken by reading the cycle counter (pmccntr_el0) directly. Any overhead should be canceled out by the measurement strategy above.

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