I wrote a script in C to allocate memory with malloc() on a infinite loop.
My aim was to realize a simple Denial of Service using WebAssembly by opening multiple tabs and make the browser crash.
I can allocate about 2 GB max for each tab to prevent the tab crash (memory limitation for x64 browsers).
#include <stdlib.h>
#define MAX_MEM 2147483630 //2 GB
int main() {
long int mem_used=209715000;
while(1){
if(mem_used<MAX_MEM){
int *ptr = malloc(sizeof(int));
mem_used+=4;
}
}
return 0;
}
I expected it to work, but instead the tab crashes.
From tests I've made, mem_used+=16 is the right choice to prevent the tab crash.
I don't know deeply the WebAssembly memory management, so my thought is that maybe it requires 4x the memory. Is that correct?
With emscripten, malloc adds some minimum chunk size and then aligns the address to at least 8 byte boundaries. So for small allocations (even zero bytes), malloc will appear to take significantly more space than needed. For big allocations, the overhead will be relatively small.
See comments in dlmalloc.c.
The following program demonstrates how much space malloc takes:
#include <iostream>
int main() {
char *previous, *current;
previous = (char*)malloc(0);
for(int i=0; i<32; ++i) {
current = (char*)malloc(i+1);
std::cout << "malloc(" << i << ") consumed " << (current-previous) << " bytes\n";
previous = current;
}
std::cout << "\n";
previous = (char*)malloc(1);
for(int i=0; i<12; ++i) {
current = (char*)malloc( 1<<(i+1) );
std::cout << "malloc(" << (1<<i) << ") consumed " << (current-previous) << " bytes\n";
previous = current;
}
return 0;
}
This yields the following output:
malloc(0) consumed 16 bytes
malloc(1) consumed 16 bytes
malloc(2) consumed 16 bytes
malloc(3) consumed 16 bytes
malloc(4) consumed 16 bytes
malloc(5) consumed 16 bytes
malloc(6) consumed 16 bytes
malloc(7) consumed 16 bytes
malloc(8) consumed 16 bytes
malloc(9) consumed 16 bytes
malloc(10) consumed 16 bytes
malloc(11) consumed 16 bytes
malloc(12) consumed 16 bytes
malloc(13) consumed 24 bytes
malloc(14) consumed 24 bytes
malloc(15) consumed 24 bytes
malloc(16) consumed 24 bytes
malloc(17) consumed 24 bytes
malloc(18) consumed 24 bytes
malloc(19) consumed 24 bytes
malloc(20) consumed 24 bytes
malloc(21) consumed 32 bytes
malloc(22) consumed 32 bytes
malloc(23) consumed 32 bytes
malloc(24) consumed 32 bytes
malloc(25) consumed 32 bytes
malloc(26) consumed 32 bytes
malloc(27) consumed 32 bytes
malloc(28) consumed 32 bytes
malloc(29) consumed 40 bytes
malloc(30) consumed 40 bytes
malloc(31) consumed 40 bytes
malloc(1) consumed 16 bytes
malloc(2) consumed 16 bytes
malloc(4) consumed 16 bytes
malloc(8) consumed 16 bytes
malloc(16) consumed 24 bytes
malloc(32) consumed 40 bytes
malloc(64) consumed 72 bytes
malloc(128) consumed 136 bytes
malloc(256) consumed 264 bytes
malloc(512) consumed 520 bytes
malloc(1024) consumed 1032 bytes
malloc(2048) consumed 2056 bytes
See full source code in this repo
Your problem is that malloc implementations typically:
a) Include overhead; and
b) Round up to some unit
malloc (sizeof(int)) is using more than sizeof(int) bytes behind the scenes.
In any system, malloc() always slightly uses more memory than you request. Emscripten uses dlmalloc, a popular malloc() implementation, as default. According to Wikipedia:
Memory on the heap is allocated as "chunks", an 8-byte aligned data structure which contains a header, and usable memory. Allocated memory contains an 8 or 16 byte overhead for the size of the chunk and usage flags. Unallocated chunks also store pointers to other free chunks in the usable space area, making the minimum chunk size 16 bytes (32-bit system) and 24 bytes (64-bit system).
This means that even a single byte allocated memory block malloc(1) uses at least 16 bytes to 24 bytes. This is because memory alignment problem and each allocated block needs additional bytes to store metadata of the block. You can easily google how malloc() works to understand why there is such overhead.
Therefore, to meet your purpose, the test should allocate much larger memory block at each iteration to minimize such overhead. I would personally recommend 4kb or 1MB instead of sizeof(int).
Related
This question already has answers here:
Sizeof struct in Go
(6 answers)
Closed 4 months ago.
I was playing around in go, and was trying to calculate and get the size of struct objects. And found something interesting, if you take a look at the following structs:
type Something struct {
anInteger int16 // 2 bytes
anotherInt int16 // 2 bytes
yetAnother int16 // 2 bytes
someBool bool // 1 byte
} // I expected 7 bytes total
type SomethingBetter struct {
anInteger int16 // 2 bytes
anotherInt int16 // 2 bytes
yetAnother int16 // 2 bytes
someBool bool // 1 byte
anotherBool bool // 1 byte
} // I expected 8 bytes total
type Nested struct {
Something // 7 bytes expected at first
completingByte bool // 1 byte
} // 8 bytes expected at first sight
But the result I got using unsafe.Sizeof(...) was as following:
Something -> 8 bytes
SomethingBetter -> 8 bytes
Nested -> 12 bytes, still, after finding out that "Something" used 8 bytes, though this might use 9 bytes
I suspect that go does something kind of like padding, but I don't know how and why it does that, is there some formula? Or logics? If it uses space padding, is it done randomly? Or based on some rules?
Yes, we have padding! if your system architecture is 32-bit the word size is 4 bytes and if it is 64-bit, the word size is 8 bytes. Now, what is the word size? "Word size" refers to the number of bits processed by a computer's CPU in one go (these days, typically 32 bits or 64 bits). Data bus size, instruction size, address size are usually multiples of the word size.
For example, suppose this struct:
type data struct {
a bool // 1 byte
b int64 // 8 byte
}
This struct it's not 9 bytes because, when our word size is 8, for first cycle, cpu reads 1 byte of bool and padding 7 bytes for others.
Imagine:
p: padding
+-----------------------------------------+----------------+
| 1-byte bool | p | p | p | p | p | p | p | int-64 |
+-----------------------------------------+----------------+
first 8 bytes second 8 bytes
For better performance, sort your struct items from bigger to small.
This is not good performance:
type data struct {
a string // 16 bytes size 16
b int32 // 4 bytes size 20
// 4 bytes padding size 24
c string // 16 bytes size 40
d int32 // 4 bytes size 44
// 4 bytes padding size 48 - Aligned on 8 bytes
}
Now It's better:
type data struct {
a string // 16 bytes size 16
c string // 16 bytes size 32
d int32 // 4 bytes size 36
b int32 // 4 bytes size 40
// no padding size 40 - Aligned on 5 bytes
}
See here for more examples.
I am trying to parallelize Monte Carlo simulation by using OpenCL. I use the MWC64X as a uniform random number generator. The code runs well on different Intel CPUs, since the output of parallel computation is very close to the sequential one.
Using OpenCL device: Intel(R) Xeon(R) CPU E5-2630L v3 # 1.80GHz
Literal influence running time: 0.029048 seconds r1 seqInfl= 0.4771
Literal influence running time: 0.029762 seconds r2 seqInfl= 0.4771
Literal influence running time: 0.029742 seconds r3 seqInfl= 0.4771
Literal influence running time: 0.02971 seconds ra seqInfl= 0.4771
Literal influence running time: 0.029225 seconds trust1-57 seqInfl= 0.6001
Literal influence running time: 0.04992 seconds trust110-1 seqInfl= 0
Literal influence running time: 0.034636 seconds trust4-57 seqInfl= 0
Literal influence running time: 0.049079 seconds trust57-110 seqInfl= 0
Literal influence running time: 0.024442 seconds trust57-4 seqInfl= 0.8026
Literal influence running time: 0.04946 seconds trust33-1 seqInfl= 0
Literal influence running time: 0.049071 seconds trust57-33 seqInfl= 0
Literal influence running time: 0.053117 seconds trust4-1 seqInfl= 0.1208
Literal influence running time: 0.051642 seconds trust57-1 seqInfl= 0
Literal influence running time: 0.052052 seconds trust57-64 seqInfl= 0
Literal influence running time: 0.052118 seconds trust64-1 seqInfl= 0
Literal influence running time: 0.051998 seconds trust57-7 seqInfl= 0
Literal influence running time: 0.052069 seconds trust7-1 seqInfl= 0
Total number of literals: 17
Sequential influence running time: 0.71728 seconds
Sequential maxInfluence Literal: trust57-4 0.8026
index1= 17 size= 51 dim1_size= 6
sum0:4781 influence0:0.478100 sum2:4781 influence2:0.478100 sum6:0 influence6:0.000000 sum10:0 sum12:0 influence12:0.000000 sum7:0 influence7:0.000000 influence10:0.000000 sum4:5962 influence4:0.596200 sum8:7971 influence8:0.797100 sum1:4781 influence1:0.478100 sum3:4781 influence3:0.478100 sum13:0 influence13:0.000000 sum11:1261 influence11:0.126100 sum9:0 influence9:0.000000 sum14:0 influence14:0.000000 sum5:0 influence5:0.000000 sum15:0 influence15:0.000000 sum16:0 influence16:0.000000
Parallel influence running time: 0.054391 seconds
Parallel maxInfluence Literal: trust57-4 Infl=0.7971
However, when I run the code on GeForce GTX 1080 Ti, with NVIDIA-SMI 430.40 and CUDA 10.1 and OpenCL 1.2 CUDA installed, the output is as below:
Using OpenCL device: GeForce GTX 1080 Ti
Influence:
Literal influence running time: 0.011119 seconds r1 seqInfl= 0.4771
Literal influence running time: 0.011238 seconds r2 seqInfl= 0.4771
Literal influence running time: 0.011408 seconds r3 seqInfl= 0.4771
Literal influence running time: 0.01109 seconds ra seqInfl= 0.4771
Literal influence running time: 0.011132 seconds trust1-57 seqInfl= 0.6001
Literal influence running time: 0.018978 seconds trust110-1 seqInfl= 0
Literal influence running time: 0.013093 seconds trust4-57 seqInfl= 0
Literal influence running time: 0.018968 seconds trust57-110 seqInfl= 0
Literal influence running time: 0.009105 seconds trust57-4 seqInfl= 0.8026
Literal influence running time: 0.018753 seconds trust33-1 seqInfl= 0
Literal influence running time: 0.018583 seconds trust57-33 seqInfl= 0
Literal influence running time: 0.02005 seconds trust4-1 seqInfl= 0.1208
Literal influence running time: 0.01957 seconds trust57-1 seqInfl= 0
Literal influence running time: 0.019686 seconds trust57-64 seqInfl= 0
Literal influence running time: 0.019632 seconds trust64-1 seqInfl= 0
Literal influence running time: 0.019687 seconds trust57-7 seqInfl= 0
Literal influence running time: 0.019859 seconds trust7-1 seqInfl= 0
Total number of literals: 17
Sequential influence running time: 0.272032 seconds
Sequential maxInfluence Literal: trust57-4 0.8026
index1= 17 size= 51 dim1_size= 6
sum0:10000 sum1:10000 sum2:10000 sum3:10000 sum4:10000 sum5:0 sum6:0 sum7:0 sum8:10000 sum9:0 sum10:0 sum11:0 sum12:0 sum13:0 sum14:0 sum15:0 sum16:0
Parallel influence running time: 0.193581 seconds
The "Influence" value equals sum*1.0/10000, thus the parallel influence only composes of 1 and 0, which is incorrect (in GPU runs) and doesn't happen when parallelizing on a Intel CPU.
When I check the output of the random number generator if(flag==0) printf("randint=%u",randint);, it seems the outputs are all zero on GPU. Below is the clinfo and the .cl code:
Device Name GeForce GTX 1080 Ti
Device Vendor NVIDIA Corporation
Device Vendor ID 0x10de
Device Version OpenCL 1.2 CUDA
Driver Version 430.40
Device OpenCL C Version OpenCL C 1.2
Device Type GPU
Device Topology (NV) PCI-E, 68:00.0
Device Profile FULL_PROFILE
Device Available Yes
Compiler Available Yes
Linker Available Yes
Max compute units 28
Max clock frequency 1721MHz
Compute Capability (NV) 6.1
Device Partition (core)
Max number of sub-devices 1
Supported partition types None
Max work item dimensions 3
Max work item sizes 1024x1024x64
Max work group size 1024
Preferred work group size multiple 32
Warp size (NV) 32
Preferred / native vector sizes
char 1 / 1
short 1 / 1
int 1 / 1
long 1 / 1
half 0 / 0 (n/a)
float 1 / 1
double 1 / 1 (cl_khr_fp64)
Half-precision Floating-point support (n/a)
Single-precision Floating-point support (core)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Correctly-rounded divide and sqrt operations Yes
Double-precision Floating-point support (cl_khr_fp64)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Address bits 64, Little-Endian
Global memory size 11720130560 (10.92GiB)
Error Correction support No
Max memory allocation 2930032640 (2.729GiB)
Unified memory for Host and Device No
Integrated memory (NV) No
Minimum alignment for any data type 128 bytes
Alignment of base address 4096 bits (512 bytes)
Global Memory cache type Read/Write
Global Memory cache size 458752 (448KiB)
Global Memory cache line size 128 bytes
Image support Yes
Max number of samplers per kernel 32
Max size for 1D images from buffer 134217728 pixels
Max 1D or 2D image array size 2048 images
Max 2D image size 16384x32768 pixels
Max 3D image size 16384x16384x16384 pixels
Max number of read image args 256
Max number of write image args 16
Local memory type Local
Local memory size 49152 (48KiB)
Registers per block (NV) 65536
Max number of constant args 9
Max constant buffer size 65536 (64KiB)
Max size of kernel argument 4352 (4.25KiB)
Queue properties
Out-of-order execution Yes
Profiling Yes
Prefer user sync for interop No
Profiling timer resolution 1000ns
Execution capabilities
Run OpenCL kernels Yes
Run native kernels No
Kernel execution timeout (NV) Yes
Concurrent copy and kernel execution (NV) Yes
Number of async copy engines 2
printf() buffer size 1048576 (1024KiB)
#define N 70 // N > index, which is the total number of literals
#define BASE 4294967296UL
//! Represents the state of a particular generator
typedef struct{ uint x; uint c; } mwc64x_state_t;
enum{ MWC64X_A = 4294883355U };
enum{ MWC64X_M = 18446383549859758079UL };
void MWC64X_Step(mwc64x_state_t *s)
{
uint X=s->x, C=s->c;
uint Xn=MWC64X_A*X+C;
uint carry=(uint)(Xn<C); // The (Xn<C) will be zero or one for scalar
uint Cn=mad_hi(MWC64X_A,X,carry);
s->x=Xn;
s->c=Cn;
}
//! Return a 32-bit integer in the range [0..2^32)
uint MWC64X_NextUint(mwc64x_state_t *s)
{
uint res=s->x ^ s->c;
MWC64X_Step(s);
return res;
}
__kernel void setInfluence(const int literals, const int size, const int dim1_size, __global int* lambdas, __global float* lambdap, __global int* dim2_size, __global float* influence){
int flag=get_global_id(0);
int sum=0;
int count=10000;
int assignment[N];
//or try to get newlambda like original version does
if(flag < literals){
mwc64x_state_t rng;
for(int i=0; i<count; i++){
for(int j=0; j<size; j++){
uint randint=MWC64X_NextUint(&rng);
float rand=randint*1.0/BASE;
//if(flag==0)
// printf("randint=%u",randint);
if(lambdap[j]<rand)
assignment[lambdas[j]]=0;
else
assignment[lambdas[j]]=1;
}
//the true case
assignment[flag]=1;
int valuet=0;
int index=0;
for(int m=0; m<dim1_size; m++){
int valueMono=1;
for(int n=0; n<dim2_size[m]; n++){
if(assignment[lambdas[index+n]]==0){
valueMono=0;
index+=dim2_size[m];
break;
}
}
if(valueMono==1){
valuet=1;
break;
}
}
//the false case
assignment[flag]=0;
int valuef=0;
index=0;
for(int m=0; m<dim1_size; m++){
int valueMono=1;
for(int n=0; n<dim2_size[m]; n++){
if(assignment[lambdas[index+n]]==0){
valueMono=0;
index+=dim2_size[m];
break;
}
}
if(valueMono==1){
valuef=1;
break;
}
}
sum += valuet-valuef;
}
influence[flag] = 1.0*sum/count;
printf("sum%d:%d\t", flag, sum);
}
}
What might be the problem when running the code on GPU? Is it MWC64X? According to its author, it can perform well on NVIDIA GPUs. If so, how can I fix it; if not, what might be the problem?
(This started out as a comment, it turns out this was the source of the problem so I'm turning it into an answer.)
You're not initialising your mwc64x_state_t rng; variable before reading from it, so any results will be undefined:
mwc64x_state_t rng;
for(int i=0; i<count; i++){
for(int j=0; j<size; j++){
uint randint=MWC64X_NextUint(&rng);
Where MWC64X_NextUint() immediately reads from the rng state before updating it:
uint MWC64X_NextUint(mwc64x_state_t *s)
{
uint res=s->x ^ s->c;
Note that you will probably want to seed your RNG differently for each work-item, otherwise you will get nasty correlation artifacts in your results.
All use-cases of a pseudo-random number are a next-level challenge in true-[PARALLEL] computing platforms (not languages, platforms).
Either, there is some source-of-randomness, which gets us into a trouble once massively-parallel requests are to get fair-handled in a truly [PARALLEL] fashion (here, hardware resources may help, yet at a cost of not being able to reproduce the same behaviour "outside" of this very same platform ( and moment-in-time, if such a source is not software-operated with some seed-injection feature, that may setup the "just"-pseudo-random algorithm that creates a pure-[SERIAL] sequence-of-produced "just"-pseudo-random numbers ) )
Or,there is some "shared"-generator of pseudo-random numbers, which enjoys of a higher level of system-wide level-of-entropy (which is good for the resulting "quality" of pseudo-randomness) but at a cost of pure-serial dependence (no parallel execution possible,serial sequence gets served one after another in a sequential manner) and having close to zero chance for repeatable runs (a must for reproducible science) providing repeatably same sequences, needed for testing and for method-validation cases.
RESUME :
The code may employ a work-item-"private" pseudo-random generating function(s) ( privacy is a must for the sake of both the parallel code-execution and the mutual independence (non-intervening processes) of generating these pseudo-random numbers ) , yet each of instances must be a) independently initialised, so as to provide the expected level of randomness achievable in parallelised code-runs and b) any such initialisation ought be performed in a repeatably reproducible manner, for the sake of running the test on different times, often using different OpenCL target computing-platforms.
For __kernel-s, that do not rely on hardware-specific sources-of-randomness, meeting the conditions a && b will suffice for receiving repeatably reproducible (same) results for testing "in vitro" and thus providing a reasonably random method for generating results during generic production-level use-case code-runs "in vivo".
The comparison of net-run-times (benchmarked above) seems to show that Amdahl's law add-on overhead costs plus a tail-end effect of the atomicity-of-work have finally decided the net-run-time was ~ 3.6x faster on XEON compared to GPU:
index1 = 17
size = 51
dim1_size = 6
sum0: 4781 influence0: 0.478100
sum2: 4781 influence2: 0.478100
sum6: 0 influence6: 0.000000
sum10: 0 influence10: 0.000000
sum12: 0 influence12: 0.000000
sum7: 0 influence7: 0.000000
sum4: 5962 influence4: 0.596200
sum8: 7971 influence8: 0.797100
sum1: 4781 influence1: 0.478100
sum3: 4781 influence3: 0.478100
sum13: 0 influence13: 0.000000
sum11: 1261 influence11: 0.126100
sum9: 0 influence9: 0.000000
sum14: 0 influence14: 0.000000
sum5: 0 influence5: 0.000000
sum15: 0 influence15: 0.000000
sum16: 0 influence16: 0.000000
Parallel influence running time: 0.054391 seconds on XEON E5-2630L v3 # 1.80GHz using OpenCL
|....
index1 = 17 |....
size = 51 |....
dim1_size = 6 |....
sum0: 10000 |....
sum1: 10000 |....
sum2: 10000 |....
sum3: 10000 |....
sum4: 10000 |....
sum5: 0 |....
sum6: 0 |....
sum7: 0 |....
sum8: 10000 |....
sum9: 0 |....
sum10: 0 |....
sum11: 0 |....
sum12: 0 |....
sum13: 0 |....
sum14: 0 |....
sum15: 0 |....
sum16: 0 |....
Parallel influence running time: 0.193581 seconds on GeForce GTX 1080 Ti using OpenCL
Configuration of the ESP32's UART_MEM_CONF_REG register does not change the size of the uart TX FIFO as expected.
I'm trying to change the size of UART0's TX FIFO o 512 Bytes.
The FIFO's size (in byte) can be set in UART_MEM_CONF_REG configuring bits 7 to bit 10. (ESP32 TRM V4.0, page 364)
This register is 0x88 by default: 128 Byte TX FIFO and 128 byte RX FIFO. So bit 7 = 1 sets 128 Byte TX FIFO size.
Unfortunately there is no info how to set Bits 7, 8,9, and 10 to change the FIFO size. My first idea was to set bit 8 for 256 bytes size, bit 9 for 512 bytes and bit 10 to 1024 bytes. I intend to use UART0 only, so there's no problem with the other UART's FIFO size.
I tried the following lines:
// Create a byte pattern to send
char buffer[256];
for (int i = 0; i < 256; i++) buffer[i] = i;
// f.e.set bit 8 for (maybe??) 256 bytes TX FIFO size, other configurations has been tested as well
WRITE_PERI_REG(UART_MEM_CONF_REG(uart_num),0x108);
// Start uart driver, no event queue, no TX ringbuffer
uart_driver_install(uart_num, UART_BUF_SIZE, 0, 0, NULL, 0);
// send 256 bytes from a buffer
uart_tx_chars(uart_num, (const char*)buffer, 256);
// but only 128 bytes are sent
At least I expected some change of the TX-FIFO size. But that's not working. The transmission ends after 128 bytes are sent out - no matter how I set the bits 7 to 10 in the UART_MEM_CONF_REG.
What's wrong, what did I miss?
I have a simple CUDA kernel which I thought was accessing global memory efficiently. The Nvidia profiler however reports that I am performing inefficient global memory accesses. My kernel code is:
__global__ void update_particles_kernel
(
float4 *pos,
float4 *vel,
float4 *acc,
float dt,
int numParticles
)
{
int index = threadIdx.x + blockIdx.x * blockDim.x;
int offset = 0;
while(index + offset < numParticles)
{
vel[index + offset].x += dt*acc[index + offset].x; // line 247
vel[index + offset].y += dt*acc[index + offset].y;
vel[index + offset].z += dt*acc[index + offset].z;
pos[index + offset].x += dt*vel[index + offset].x; // line 251
pos[index + offset].y += dt*vel[index + offset].y;
pos[index + offset].z += dt*vel[index + offset].z;
offset += blockDim.x * gridDim.x;
}
In particular the profiler reports the following:
From the CUDA best practices guide it says:
"For devices of compute capability 2.x, the requirements can be summarized quite easily: the concurrent accesses of the threads of a warp will coalesce into a number of transactions equal to the number of cache lines necessary to service all of the threads of the warp. By default, all accesses are cached through L1, which as 128-byte lines. For scattered access patterns, to reduce overfetch, it can sometimes be useful to cache only in L2, which caches shorter 32-byte segments (see the CUDA C Programming Guide).
For devices of compute capability 3.x, accesses to global memory are cached only in L2; L1 is reserved for local memory accesses. Some devices of compute capability 3.5, 3.7, or 5.2 allow opt-in caching of globals in L1 as well."
Now in my kernel based on this information I would expect that 16 accesses would be required to service a 32 thread warp because float4 is 16 bytes and on my card (770m compute capability 3.0) reads from the L2 cache are performed in 32 bytes chunks (16 bytes * 32 threads / 32 bytes cache lines = 16 accesses). Indeed as you can see the profiler reports that I am doing 16 access. What I don't understand is why the profiler reports that the ideal access would involve 8 L2 transactions per access for line 247 and only 4 L2 transactions per access for the remaining lines. Can someone explain what I am missing here?
I have a simple CUDA kernel which I thought was accessing global memory efficiently. The Nvidia profiler however reports that I am performing inefficient global memory accesses.
To take one example, your float4 vel array is stored in memory like this:
0.x 0.y 0.z 0.w 1.x 1.y 1.z 1.w 2.x 2.y 2.z 2.w 3.x 3.y 3.z 3.w ...
^ ^ ^ ^ ...
thread0 thread1 thread2 thread3
So when you do this:
vel[index + offset].x += ...; // line 247
you are accessing (storing) at the locations (.x) that I have marked above. The gaps in between each ^ mark indicate an inefficient access pattern, which the profiler is pointing out. (It does not matter that in the very next line of code, you are storing to the .y locations.)
There are at least 2 solutions, one of which would be a classical AoS -> SoA reorganization of your data, with appropriate code adjustments. This is well documented (e.g. here on the cuda tag and elsewhere) in terms of what it means, and how to do it, so I will let you look that up.
The other typical solution is to load a float4 quantity per thread, when you need it, and store a float4 quantity per thread, when you need to. Your code can be trivially reworked to do this, which should give improved profiling results:
//preceding code need not change
while(index + offset < numParticles)
{
float4 my_vel = vel[index + offset];
float4 my_acc = acc[index + offset];
my_vel.x += dt*my_acc.x;
my_vel.y += dt*my_acc.y;
my_vel.z += dt*my_acc.z;
vel[index + offset] = my_vel;
float4 my_pos = pos[index + offset];
my_pos.x += dt*my_vel.x;
my_pos.y += dt*my_vel.y;
my_pos.z += dt*my_vel.z;
pos[index + offset] = my_pos;
offset += blockDim.x * gridDim.x;
}
Even though you might think that this code is "less efficient" than your code, because your code "appears" to be only loading and storing .x, .y, .z, whereas mine "appears" to also load and store .w, in fact there is essentially no difference, due to the way a GPU loads and stores to/from global memory. Although your code does not appear to touch .w, in the process of accessing the adjacent elements, the GPU will load the .w elements from global memory, and also (eventually) store the .w elements back to global memory.
What I don't understand is why the profiler reports that the ideal access would involve 8 L2 transactions per access for line 247
For line 247 in your original code, you are accessing one float quantity per thread for the load operation of acc.x, and one float quantity per thread for the load operation of vel.x. A float quantity per thread by itself should require 128 bytes for a warp, which is 4 32-byte L2 cachelines. Two loads together would require 8 L2 cacheline loads. This is the ideal case, which assumes that the quantities are packed together nicely (SoA). But that is not what you have (you have AoS).
I stumbled upon an interesting thing while checking performance of memory allocation in GO.
package main
import (
"fmt"
"time"
)
func main(){
const alloc int = 65536
now := time.Now()
loop := 50000
for i := 0; i<loop;i++{
sl := make([]byte, alloc)
i += len(sl) * 0
}
elpased := time.Since(now)
fmt.Printf("took %s to allocate %d bytes %d times", elpased, alloc, loop)
}
I am running this on a Core-i7 2600 with go version 1.6 64bit (also same results on 32bit) and 16GB of RAM (on WINDOWS 10)
so when alloc is 65536 (exactly 64K) it runs for 30 seconds (!!!!).
When alloc is 65535 it takes ~200ms.
Can someone explain this to me please?
I tried the same code at home with my core i7-920 # 3.8GHZ but it didn't show same results (both took around 200ms). Anyone has an idea what's going on?
Setting GOGC=off improved performance (down to less than 100ms). Why?
becaue of escape analysis. When you build with go build -gcflags -m the compiler prints whatever allocations escapes to heap. It really depends on your machine and GO compiler version but when the compiler decides that the allocation should move to heap it means 2 things:
1. the allocation will take longer (since "allocating" on the stack is just 1 cpu instruction)
2. the GC will have to clean up that memory later - costing more CPU time
for my machine, the allocation of 65536 bytes escapes to heap and 65535 doesn't.
that's why 1 bytes changed the whole proccess from 200ms to 30s. Amazing..
Note/Update 2021: as Tapir Liui notes in Go101 with this tweet:
As of Go 1.17, Go runtime will allocate the elements of slice x on stack if the compiler proves they are only used in the current goroutine and N <= 64KB:
var x = make([]byte, N)
And Go runtime will allocate the array y on stack if the compiler proves it is only used in the current goroutine and N <= 10MB:
var y [N]byte
Then how to allocated (the elements of) a slice which size is larger than 64KB but not larger than 10MB on stack (and the slice is only used in one goroutine)?
Just use the following way:
var y [N]byte
var x = y[:]
Considering stack allocation is faster than heap allocation, that would have a direct effect on your test, for alloc equals to 65536 and more.
Tapir adds:
In fact, we could allocate slices with arbitrary sum element sizes on stack.
const N = 500 * 1024 * 1024 // 500M
var v byte = 123
func createSlice() byte {
var s = []byte{N: 0}
for i := range s { s[i] = v }
return s[v]
}
Changing 500 to 512 make program crash.
the reason is very simple.
const alloc int = 65535
0x0000 00000 (example.go:8) TEXT "".main(SB), ABIInternal, $65784-0
const alloc int = 65536
0x0000 00000 (example.go:8) TEXT "".main(SB), ABIInternal, $248-0
the difference is where the slice are created.