I'm doing an experiment to see which ARM SVE vector length would be the best for my chip design, or to help select which chip has the optimal vector length for my application.
How to change the vector length in a gem5 simulation to see how it affects workload performance?
For SE:
se.py --param 'system.cpu[:].isa[:].sve_vl_se = 2'
For FS:
fs.py --param 'system.sve_vl = 2'
where the values are given in multiples of 128 bits, so 2 means length 256.
You can test this easily with the ADDVL instruction as shown in this example.
The name of those parameters can be easily determined by looking at a m5out/config.ini generated from a previous run.
Note however that this value is architecturally visible, and so it might not be possible to checkpoint after Linux boot, and restore with a different vector length than the boot, to speed up experiments. This is likely true in general even though the kernel itself does not run vector instructions, because there is software control of the effective vector length. Maybe it is possible to set a big vector length on the simulator to start with and then tell Linux to reduce it somehow in software, but I'm not sure what's the API.
Tested in gem5 3126e84db773f64e46b1d02a9a27892bf6612d30.
To change the vector length, one can use command line option:
--arm-sve-vl=<vl in quadwords: one of {1, 2, 4, 8, 16}>
where vl is a multiple of 128. So for a simulation of 512-bit SVE machine, one should use:
--arm-sve-vl=4
This works both for Syscall-Emulation mode and Full System mode.
If one wants to quickly explore the space of different vector lengths, one can also change it during the simulation (only in Full system mode). For example, to change the SVE length to 256, put the following line in your bootscript, before running the benchmark:
echo 256 >/proc/sys/abi/sve_default_vector_length
You can get more information on https://www.rico.cat/files/ICS18-gem5-sve-tutorial.pdf.
Related
We are benchmarking 2D FFT performance on an NVIDIA A100 in order to determine which sizes have the best performance. The following shows how the runtime for each size is performed. GPU memroy is cleared after each size is run.
def run_fft():
fft2(array, axes=(-2, -1), overwrite_x=True)
timing = cupyx.timing.repeat(run_fft, repeat=10, n_warmup=1)
Running it across a range of possible sizes results in the measurements below .
As you can see, there seems to be a set of sizes that are slower than the rest (the quasi-linear streaks above the main sequence). These sizes also include ones which are factors of low prime numbers (such as 2 and 3). I was wondering whether there is a general rule to define which 2D FFT sizes run optimally (for example, for the CPU case and when using fftw3, the general rule is defined here:.
To all homomorphic encryption experts out there:
I'm using the PALISADE library:
int plaintextModulus = 65537;
float sigma = 3.2;
SecurityLevel securityLevel = HEStd_128_classic;
uint32_t depth = 2;
//Instantiate the crypto context
CryptoContext<DCRTPoly> cc = CryptoContextFactory<DCRTPoly>::genCryptoContextBFVrns(
plaintextModulus, securityLevel, sigma, 0, depth, 0, OPTIMIZED);
could you please explain (all) the parameters especially intrested in ptm, depth and sigma.
Secondly I am trying to make a Packed Plaintext with the cc above.
cc->MakePackedPlaintext(array);
What is the maximum size of the array? On my local machine (8GB RAM) when the array is larger than ~8000 int64 I get an free(): invalid next size (normal) error
Thank you for asking the question.
Plaintext modulus t (denoted as t here) is a critical parameter for BFV as all operations are performed mod t. In other words, when you choose t, you have to make sure that all computations do not wrap around, i.e., do not exceed t. Otherwise you will get an incorrect answer unless your goal is to compute something mod t.
sigma is the distribution parameter (used for the underlying Learning with Errors problem). You can just set to 3.2. No need to change it.
Depth is the multiplicative depth of the circuit you are trying to compute. It has nothing to with the size of vectors. Basically, if you have AxBxCxD, you have a depth 3 with a naive approach. BFV also supports more efficient binary tree evaluation, i.e., (AxB)x(CxD) - this option will reduce the depth to 2.
BFV is a scheme that supports packing. By default, the size of packed ciphertext is equal to the ring dimension (something like 8192 for the example you mentioned). This means you can pack up to 8192 integers in your case. To support larger arrays/vectors, you would need to break them into batches of 8192 each and encrypt each one separately.
Regarding your application, the CKKS scheme would probably be a much better option (I will respond on the application in more detail in the other thread).
I have some experience with the SEAL library which also uses the BFV encryption scheme. The BFV scheme uses modular arithmetic and is able to encrypt integers (not real numbers).
For the parameters you're asking about:
The Plaintext Modulus is an upper bound for the input integers. If this parameter is too low, it might cause your integers to overflow (depending on how large they are of course)
The Sigma is the distribution parameter for Gaussian noise generation
The Depth is the circuit depth which is the maximum number of multiplications on a path
Also for the Packed Plaintext, you should use vectors not arrays. Maybe that will fix your problem. If not, try lowering the size and make several vectors if necessary.
You can determine the ring dimension (generated by the crypto context based on your parameter settings) by using cc->GetRingDimension() as shown in line 113 of https://gitlab.com/palisade/palisade-development/blob/master/src/pke/examples/simple-real-numbers.cpp
For opencl optimization, my idea is try to make match for
1 workgroup(kernel coding) as compute unit(GPU Hardware)
1 workitem(kernel coding) as process element(GPU Hardware)
( Maybe my idea is not correct, please teach me )
for example:
1. I have a global work size of 4000 by 3000.
2. My GPU opnecl device has a maximum work-group-size of 8192.
3. I call clEnqueueNDRangeKernel with the desired local-work-size (along with all other necessary parameters)
4. by fucntion call:
a. clGetKernelWorkGroupInfo(kernel, device, CL_KERNEL_WORK_GROUP_SIZE, sizeof(size_t), (void*)&workGroupSizeUsed, NULL);
b. clGetKernelWorkGroupInfo(kernel, device, CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE, sizeof(size_t), (void*)&workGroupSizeUsed, NULL);
above a and b are return 8192.
maximum work-group-size, CL_KERNEL_WORK_GROUP_SIZE, CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE all are 8192.
I have no idea what I should follow to define my local work size...
(Q1)Any good idea for setting the local work size? (10x10? 40x30, X by Y )
clEnqueueNDRangeKernel(command_queue, kernel, 2, NULL, global_work_item_size, local_work_item_size, 0, NULL, NULL);
Very headache to define this "local_work_item_size" of clEnqueueNDRangeKernel function.
(Q2)
Could some one explain the difference if I set local work size = 1,1 between
local work size = 4000,3000 ?
Thank you in advance!
(Q1)Any good idea for setting the local work size? (10x10? 40x30, X by Y )
As pmdj pointed out, this highly depends on your application. Since it is unclear how you selected your global_work_size and it is also linked to the local_work_size I would like to explain that one first. Usually what you would want to do is to map the size of the data you want to process to the global_work_size. E.g. if you have an array with 1024 values you would also pick a global_work_size of 1024 because then you can easily use the global id as an index in your OpenCL program:
int index = get_global_id(0);
input_array[index]++; // your data processing
However, the global_work_size is limited to a maximum 2^32 - 1. If you have more data to process than that you can pass your global_work_size and data size as parameters and use a loop like the following one:
int index = get_global_id(0);
for (int i = index; i < data_size; i += global_work_size) {
input_array[i]++; // your data processing
}
The last fact which is important for the global_work_size is that it needs to be dividable by the local_work_size. This can result into a your global_work_size being bigger than your data size, e.g. you could have 1000 values while your local_work_size is 32. Then you would make your global_work_size 1024 and ensure through a condition like the one above (i < data_size) that the redundant work items are not doing anything weird like accessing not allocated memory areas.
The local_work_size depends on your platform. First of all you should always have a local_work_size which is a multiple of 32 for NVIDIA or a multiple of 64 for AMD GPUs. This represents the amount of operations which are scheduled together anyway. If you use a different number the GPU will have idle threads which won't do anything but decrease your performance.
Not only the manufacturer but also the specific type of your GPU has to be considered to find the optimal local_work_size. The global_work_size divided by the local_work_size is the number of work groups. Each work group is executed by one thread inside your CPU/GPU. If you use OpenCL to run your application on powerful hardware you want to make sure that it runs as parallel as possible. E.g. if you use an Intel i7 with 8 threads you would want to make sure that you have at least 8 work groups (global_work_size / local_work_size >= 8). If you use a NVIDIA GeForce GTX 1060 with 1280 CUDA Cores you would want to have at least 1280 work groups. But never at the cost of having a local_work_size of less than 32 which is more important!
If you are having more work groups than your hardware has threads that does not matter, they will be processed sequentially. Hence for most applications you can always set your local_work_size to 32/64. The only exception is if you require synchronization among more than work items. E.g. barriers only work inside work groups but not among different work groups. An example: If you need to to sum up chunks of 1024 values before being able to proceed with your algorithm you would need to set your local_work_size to 1024 for the barrier to work as desired.
(Q2) Could some one explain the difference if I set local work size = 1,1 between local work size = 4000,3000 ?
Both, the global_work_size and the local_work_size can have more than one dimension. If this is used or not solely depends on the preference of the programmer. All algorithms can be implemented in one dimension as well and the number of work groups is calculated by multiplying the dimensions, e.g. if your global_work_size is 20*20 and your local_work_size is 10*10 you would run the program with (20*20) / (10*10) = 400 work groups.
I personally like to use the dimensions if I am processing data which has multiple dimensions. Imagine your input is a two-dimensional image, you could simply use its width and height as global_work_size (e.g. 1024 * 1024) and the local_work_size accordingly (e.g. 32 * 32). In your code you could then use the following indices:
int x = get_global_id(0);
int y = get_global_id(1);
input_array[x][y]++; // your data processing
I'm attempting to unpack bytes from an input file in GNU Radio Companion into a binary bitstream. My problem is that the Unpack K Bits block works at the same sample rate as the file source. So by the time the first bit of byte 1 is clocked out, byte 2 has already been loaded. How do I either slow down the file source or speed up the Unpack K Bits block? Is there a way I can tell GNU Radio Companion to repeat each byte from the file source 8 times?
Note that "after pack" is displaying 4 times as much data as "before pack".
My problem is that the Unpack K Bits block works at the same sample rate as the file source
No it doesn't. Unpack K Bits is an interpolator block. In your case the interpolation is 8. For every bytes 8 new bytes are produced.
The result is right, but the time scale of your sink is wrong. You have to change the sampling rate at the second GUI Time Sink to fit the true sampling rate of the flowgraph after the Unpack K Bits.
So instead of 32e3 it should be 8*32e3.
Manos' answer is very good, but I want to add to this:
This is a common misunderstanding for people that just got in touch with doing digital signal processing down at the sample layer:
GNU Radio doesn't have a notion of sampling rate itself. The term sampling rate is only used by certain blocks to e.g. calculate the period of a sine (in the case of the signal source: Period = f_signal/f_sample), or to calculate times or frequencies that are written on display axes (like in your case).
"Slowing down" means "making the computer process samples slower", but doesn't change the signal.
All you need to do is match what you want the displaying sink to show as time units with what you configure it to do.
Edit: Proposed solutions results are added at the end of the question.
I'm starting to program with OpenCL, and I have created a naive implementation of my problem.
The theory is: I have a 3D grid of elements, where each elements has a bunch of information (around 200 bytes). Every step, every element access its neighbors information and accumulates this information to prepare to update itself. After that there is a step where each element updates itself with the information gathered before. This process is executed iteratively.
My OpenCL implementation is: I create an OpenCL buffer of 1 dimension, fill it with structs representing the elements, which have an "int neighbors 6 " where I store the index of the neighbors in the Buffer. I launch a kernel that consults the neighbors and accumulate their information into element variables not consulted in this step, and then I launch another kernel that uses this variables to update the elements. These kernels use __global variables only.
Sample code:
typedef struct{
float4 var1;
float4 var2;
float4 nextStepVar1;
int neighbors[8];
int var3;
int nextStepVar2;
bool var4;
} Element;
__kernel void step1(__global Element *elements, int nelements){
int id = get_global_id(0);
if (id >= nelements){
return;
}
Element elem = elements[id];
for (int i=0; i < 6; ++i){
if (elem.neighbors[i] != -1){
//Gather information of the neighbor and accumulate it in elem.nextStepVars
}
}
elements[id] = elem;
}
__kernel void step2(__global Element *elements, int nelements){
int id = get_global_id(0);
if (id >= nelements){
return;
}
Element elem = elements[id];
//update elem variables by using elem.nextStepVariables
//restart elem.nextStepVariables
}
Right now, my OpenCL implementation takes basically the same time than my C++ implementation.
So, the question is: How would you (the experts :P) address this problem?
I have read about 3D images, to store the information and change the neighborhood accessing pattern by changing the NDRange to a 3D one. Also, I have read about __local memory, to first load all the neighborhood in a workgroup, synchronize with a barrier and then use them, so that accesses to memory are reduced.
Could you give me some tips to optimize a process like the one I described, and if possible, give me some snippets?
Edit: Third and fifth optimizations proposed by Huseyin Tugrul were already in the code. As mentioned here, to make structs behave properly, they need to satisfy some restrictions, so it is worth understanding that to avoid headaches.
Edit 1: Applying the seventh optimization proposed by Huseyin Tugrul performance increased from 7 fps to 60 fps. In a more general experimentation, the performance gain was about x8.
Edit 2: Applying the first optimization proposed by Huseyin Tugrul performance increased about x1.2 . I think that the real gain is higher, but hides because of another bottleneck not yet solved.
Edit 3: Applying the 8th and 9th optimizations proposed by Huseyin Tugrul didn't change performance, because of the lack of significant code taking advantage of these optimizations, worth trying in other kernels though.
Edit 4: Passing invariant arguments (such as n_elements or workgroupsize) to the kernels as #DEFINEs instead of kernel args, as mentioned here, increased performance around x1.33. As explained in the document, this is because of the aggressive optimizations that the compiler can do when knowing the variables at compile-time.
Edit 5: Applying the second optimization proposed by Huseyin Tugrul, but using 1 bit per neighbor and using bitwise operations to check if neighbor is present (so, if neighbors & 1 != 0, top neighbor is present, if neighbors & 2 != 0, bot neighbor is present, if neighbors & 4 != 0, right neighbor is present, etc), increased performance by a factor of x1.11. I think this was mostly because of the data transfer reduction, because the data movement was, and keeps being my bottleneck. Soon I will try to get rid of the dummy variables used to add padding to my structs.
Edit 6: By eliminating the structs that I was using, and creating separated buffers for each property, I eliminated the padding variables, saving space, and was able to optimize the global memory access and local memory allocation. Performance increased by a factor of x1.25, which is very good. Worth doing this, despite the programmatic complexity and unreadability.
According to your step1 and step2, you are not making your gpu core work hard. What is your kernel's complexity? What is your gpu usage? Did you check with monitoring programs like afterburner? Mid-range desktop gaming cards can get 10k threads each doing 10k iterations.
Since you are working with only neighbours, data size/calculation size is too big and your kernels may be bottlenecked by vram bandiwdth. Your main system ram could be as fast as your pci-e bandwidth and this could be the issue.
1) Use of Dedicated Cache could be getting you thread's actual grid cell into private registers that is fastest. Then neighbours into __local array so the comparisons/calc only done in chip.
Load current cell into __private
Load neighbours into __local
start looping for local array
get next neighbour into __private from __local
compute
end loop
(if it has many neighbours, lines after "Load neighbours into __local" can be in another loop that gets from main memory by patches)
What is your gpu? Nice it is GTX660. You should have 64kB controllable cache per compute unit. CPUs have only registers of 1kB and not addressable for array operations.
2) Shorter Indexing could be using a single byte as index of neighbour stored instead of int. Saving precious L1 cache space from "id" fetches is important so that other threads can hit L1 cache more!
Example:
0=neighbour from left
1=neighbour from right
2=neighbour from up
3=neighbour from down
4=neighbour from front
5=neighbour from back
6=neighbour from upper left
...
...
so you can just derive neighbour index from a single byte instead of 4-byte int which decreases main memory accessing for at least neighbour accessing. Your kernel will derive neighbour index from upper table using its compute power, not memory power because you would make this from core registers(__privates). If your total grid size is constant, this is very easy such as just adding 1 actual cell id, adding 256 to id or adding 256*256 to id or so.
3) Optimum Object Size could be making your struct/cell-object size a multiple of 4 bytes. If your total object size is around 200-bytes, you can pad it or augment it with some empty bytes to make exactly 200 bytes, 220Bytes or 256 bytes.
4) Branchless Code (Edit: depends!) using less if-statements. Using if-statement makes computation much slower. Rather than checking for -1 as end of neightbour index , you can use another way . Becuase lightweight core are not as capable of heavyweight. You can use surface-buffer-cells to wrap the surface so computed-cells will have always have 6-neighbours so you get rid of if (elem.neighbors[i] != -1) . Worth a try especially for GPU.
Just computing all neighbours are faster rather than doing if-statement. Just multiply the result change with zero when it is not a valid neighbour. How can we know that it is not a valid neighbour? By using a byte array of 6-elements per cell(parallel to neighbour id array)(invalid=0, valid=1 -->multiply the result with this)
The if-statement is inside a loop which counting for six times. Loop unrolling gives similar speed-up if the workload in the loop is relatively easy.
But, if all threads within same warp goes into same if-or-else branch, they don't lose performance. So this depends wheter your code diverges or not.
5) Data Elements Reordering you can move the int[8] element to uppermost side of struct so memory accessing may become more yielding so smaller sized elements to lower side can be read in a single read-operation.
6) Size of Workgroup trying different local workgroup size can give 2-3x performance. Starting from 16 until 512 gives different results. For example, AMD GPUs like integer multiple of 64 while NVIDIA GPUs like integer multiple of 32. INTEL does fine at 8 to anything since it can meld multiple compute units together to work on same workgroup.
7) Separation of Variables(only if you cant get rid of if-statements) Separation of comparison elements from struct. This way you dont need to load a whole struct from main memory just to compare an int or a boolean. When comparison needs, then loads the struct from main memory(if you have local mem optimization already, then you should put this operation before it so loading into local mem is only done for selected neighbours)
This optimisation makes best case(no neighbour or only one eighbour) considerably faster. Does not affect worst case(maximum neighbours case).
8a) Magic Using shifting instead of dividing by power of 2. Doing similar for modulo. Putting "f" at the end of floating literals(1.0f instead of 1.0) to avoid automatic conversion from double to float.
8b) Magic-2 -cl-mad-enable Compiler option can increase multiply+add operation speed.
9) Latency Hiding Execution configuration optimization. You need to hide memory access latency and take care of occupancy.
Get maximum cycles of latency for instructions and global memory access.
Then divide memory latency by instruction latency.
Now you have the ratio of: arithmetic instruction number per memory access to hide latency.
If you have to use N instructions to hide mem latency and you have only M instructions in your code, then you will need N/M warps(wavefronts?) to hide latency because a thread in gpu can do arithmetics while other thread getting things from mem.
10) Mixed Type Computing After memory access is optimized, swap or move some instructions where applicable to get better occupancy, use half-type to help floating point operations where precision is not important.
11) Latency Hiding again Try your kernel code with only arithmetics(comment out all mem accesses and initiate them with 0 or sometihng you like) then try your kernel code with only memory access instructions(comment out calculations/ ifs)
Compare kernel times with original kernel time. Which is affeecting the originatl time more? Concentrate on that..
12) Lane & Bank Conflicts Correct any LDS-lane conflicts and global memory bank conflicts because same address accessings can be done in a serialed way slowing process(newer cards have broadcast ability to reduce this)
13) Using registers Try to replace any independent locals with privates since your GPU can give nearly 10TB/s throughput using registers.
14) Not Using Registers Dont use too many registers or they will spill to global memory and slow the process.
15) Minimalistic Approach for Occupation Look at local/private usage to get an idea of occupation. If you use much more local and privates then less threads can be utilized in same compute unit and leading lesser occupation. Less resource usage leads higher chance of occupation(if you have enough total threads)
16) Gather Scatter When neighbours are different particles(like an nbody NNS) from random addresses of memory, its maybe hard to apply but, gather read optimization can give 2x-3x speed on top of before optimizations (needs local memory optimization to work) so it reads in an order from memory instead of randomly and reorders as needed in the local memory to share between (scatter) to threads.
17) Divide and Conquer Just in case when buffer is too big and copied between host and device so makes gpu wait idle, then divide it in two, send them separately, start computing as soon as one arrives, send results back concurrently in the end. Even a process-level parallelism could push a gpu to its limits this way. Also L2 cache of GPU may not be enough for whole of data. Cache-tiled computing but implicitly done instead of direct usage of local memory.
18) Bandwidth from memory qualifiers. When kernel needs some extra 'read' bandwidth, you can use '__constant'(instead of __global) keyword on some parameters which are less in size and only for reading. If those parameters are too large then you can still have good streaming from '__read_only' qualifier(after the '__global' qualifier). Similary '__write_only' increases throughput but these give mostly hardware-specific performance. If it is Amd's HD5000 series, constant is good. Maybe GTX660 is faster with its cache so __read_only may become more usable(or Nvidia using cache for __constant?).
Have three parts of same buffer with one as __global __read_only, one as __constant and one as just __global (if building them doesn't penalty more than reads' benefits).
Just tested my card using AMD APP SDK examples, LDS bandwidth shows 2TB/s while constant is 5TB/s(same indexing instead of linear/random) and main memory is 120 GB/s.
Also don't forget to add restrict to kernel parameters where possible. This lets compiler do more optimizations on them(if you are not aliasing them).
19) Modern hardware transcendental functions are faster than old bit hack (like Quake-3 fast inverse square root) versions
20) Now there is Opencl 2.0 which enables spawning kernels inside kernels so you can further increase resolution in a 2d grid point and offload it to workgroup when needed (something like increasing vorticity detail on edges of a fluid dynamically)
A profiler can help for all those, but any FPS indicator can do if only single optimization is done per step.
Even if benchmarking is not for architecture-dependent code paths, you could try having a multiple of 192 number of dots per row in your compute space since your gpu has multiple of that number of cores and benchmark that if it makes gpu more occupied and have more gigafloatingpoint operations per second.
There must be still some room for optimization after all these options, but idk if it damages your card or feasible for production time of your projects. For example:
21) Lookup tables When there is 10% more memory bandwidth headroom but no compute power headroom, offload 10% of those workitems to a LUT version such that it gets precomputed values from a table. I didn't try but something like this should work:
8 compute groups
2 LUT groups
8 compute groups
2 LUT groups
so they are evenly distributed into "threads in-flight" and get advantage of latency hiding stuff. I'm not sure if this is a preferable way of doing science.
21) Z-order pattern For traveling neighbors increases cache hit rate. Cache hit rate saves some global memory bandwidth for other jobs so that overall performance increases. But this depends on size of cache, data layout and some other things I don't remember.
22) Asynchronous Neighbor Traversal
iteration-1: Load neighbor 2 + compute neighbor 1 + store neighbor 0
iteration-2: Load neighbor 3 + compute neighbor 2 + store neighbor 1
iteration-3: Load neighbor 4 + compute neighbor 3 + store neighbor 2
so each body of loop doesn't have any chain of dependency and fully pipelined on GPU processing elements and OpenCL has special instructions for asynchronously loading/storing global variables using all cores of a workgroup. Check this:
https://www.khronos.org/registry/OpenCL/sdk/1.0/docs/man/xhtml/async_work_group_copy.html
Maybe you can even divide computing part into two and have one part use transcandental functions and other part use add/multiply so that add/multiply operations don't wait for a slow sqrt. If there are at least several neighbors to traveerse, this should hide some latency behind other iterations.