Linux OS: /proc/[pid]/smaps vs /proc/[pid]/statm - linux

I would like calculate the memory usage for single process. So after a little bit of research I came across over smaps and statm.
First of all what is smaps and statm? What is the difference?
statm has a field RSS and in smaps I sum up all RSS values. But those values are different for the same process. I know that statm measures in pages. For comparison purposes I converted that value in kb as in smaps. But those values are not equal.
Why do these two values differ, even though they represent the rss value for the same process?
statm
232214 80703 7168 27 0 161967 0 (measured in pages, pages size is 4096)
smaps
Rss 1956
My aim is to calculate the memory usage for a single process. I am interested in two values. USS and PSS.
Can I gain those two values by just using smaps? Is that value correct?
Also, I would like to return that value as percentage.

I think statm is an approximated simplification of smaps, which is more expensive to get. I came to this conclusion after I looked at the source:
smaps
The information you see in smaps is defined in /fs/proc/task_mmu.c:
static int show_smap(struct seq_file *m, void *v, int is_pid)
{
(...)
struct mm_walk smaps_walk = {
.pmd_entry = smaps_pte_range,
.mm = vma->vm_mm,
.private = &mss,
};
memset(&mss, 0, sizeof mss);
walk_page_vma(vma, &smaps_walk);
show_map_vma(m, vma, is_pid);
seq_printf(m,
(...)
"Rss: %8lu kB\n"
(...)
mss.resident >> 10,
The information in mss is used by walk_page_vma defined in /mm/pagewalk.c. However, the mss member resident is not filled in walk_page_vma - instead, walk_page_vma calls callback specified in smaps_walk:
.pmd_entry = smaps_pte_range,
.private = &mss,
like this:
if (walk->pmd_entry)
err = walk->pmd_entry(pmd, addr, next, walk);
So what does our callback, smaps_pte_range in /fs/proc/task_mmu.c, do?
It calls smaps_pte_entry and smaps_pmd_entry in some circumstances, out of which both call statm_account(), which in turn... upgrades resident size! All of these functions are defined in the already linked task_mmu.c so I didn't post relevant code snippets as they can be easily seen in the linked sources.
PTE stands for Page Table Entry and PMD is Page Middle Directory. So basically we iterate through the page entries associated with given process and update RAM usage depending on the circumstances.
statm
The information you see in statm is defined in /fs/proc/array.c:
int proc_pid_statm(struct seq_file *m, struct pid_namespace *ns,
struct pid *pid, struct task_struct *task)
{
unsigned long size = 0, resident = 0, shared = 0, text = 0, data = 0;
struct mm_struct *mm = get_task_mm(task);
if (mm) {
size = task_statm(mm, &shared, &text, &data, &resident);
mmput(mm);
}
seq_put_decimal_ull(m, 0, size);
seq_put_decimal_ull(m, ' ', resident);
seq_put_decimal_ull(m, ' ', shared);
seq_put_decimal_ull(m, ' ', text);
seq_put_decimal_ull(m, ' ', 0);
seq_put_decimal_ull(m, ' ', data);
seq_put_decimal_ull(m, ' ', 0);
seq_putc(m, '\n');
return 0;
}
This time, resident is filled by task_statm. This one has two implementations, one in /fs/proc/task_mmu.c and second in /fs/proc/task_nomm.c. Since they're almost surely mutually exclusive, I'll focus on the implementation in task_mmu.c (which also contained task_smaps). In this implementation we see that
unsigned long task_statm(struct mm_struct *mm,
unsigned long *shared, unsigned long *text,
unsigned long *data, unsigned long *resident)
{
*shared = get_mm_counter(mm, MM_FILEPAGES);
(...)
*resident = *shared + get_mm_counter(mm, MM_ANONPAGES);
return mm->total_vm;
}
it queries some counters, namely, MM_FILEPAGES and MM_ANONPAGES. These counters are modified during different operations on memory such as do_wp_page defined at /mm/memory.c. All of the modifications seem to be done by the files located in /mm/ and there seem to be quite a lot of them, so I didn't include them here.
Conclusion
smaps does complicated iteration through all referenced memory regions and updates resident size using the collected information. statm uses data that was already calculated by someone else.
The most important part is that while smaps collects the data each time in an independent manner, statm uses counters that get incremented or decremented during process life cycle. There are a lot of places that need to do the bookkeeping, and perhaps some places don't upgrade the counters like they should. That's why IMO statm is inferior to smaps, even if it takes fewer CPU cycles to complete.
Please note that this is the conclusion I drew based on common sense, but I might be wrong - perhaps there are no internal inconsistencies in counter decrementing and incrementing, and instead, they might count some pages differently than smaps. At this point I believe it'd be wise to take it to some experienced kernel maintainers.

Related

How to pass efficiently the hugepages-backed buffer to the BM DMA device in Linux?

I need to provide a huge circular buffer (a few GB) for the bus-mastering DMA PCIe device implemented in FPGA.
The buffers should not be reserved at the boot time. Therefore, the buffer may be not contiguous.
The device supports scatter-gather (SG) operation, but for performance reasons, the addresses and lengths of consecutive contiguous segments of the buffer are stored inside the FPGA.
Therefore, usage of standard 4KB pages is not acceptable (there would be up to 262144 segments for each 1GB of the buffer).
The right solution should allocate the buffer consisting of 2MB hugepages in the user space (reducing the maximum number of segments by factor of 512).
The virtual address of the buffer should be transferred to the kernel driver via ioctl. Then the addresses and the length of the segments should be calculated and written to the FPGA.
In theory, I could use get_user_pages to create the list of the pages, and then call sg_alloc_table_from_pages to obtain the SG list suitable to program the DMA engine in FPGA.
Unfortunately, in this approach I must prepare the intermediate list of page structures with length of 262144 pages per 1GB of the buffer. This list is stored in RAM, not in the FPGA, so it is less problematic, but anyway it would be good to avoid it.
In fact I don't need to keep the pages maped for the kernel, as the hugepages are protected against swapping out, and they are mapped for the user space application that will process the received data.
So what I'm looking for is a function sg_alloc_table_from_user_hugepages, that could take such a user-space address of the hugepages-based memory buffer, and transfer it directly into the right scatterlist, without performing unnecessary and memory-consuming mapping for the kernel.
Of course such a function should verify that the buffer indeed consists of hugepages.
I have found and read these posts: (A), (B), but couldn't find a good answer.
Is there any official method to do it in the current Linux kernel?
At the moment I have a very inefficient solution based on get_user_pages_fast:
int sgt_prepare(const char __user *buf, size_t count,
struct sg_table * sgt, struct page *** a_pages,
int * a_n_pages)
{
int res = 0;
int n_pages;
struct page ** pages = NULL;
const unsigned long offset = ((unsigned long)buf) & (PAGE_SIZE-1);
//Calculate number of pages
n_pages = (offset + count + PAGE_SIZE - 1) >> PAGE_SHIFT;
printk(KERN_ALERT "n_pages: %d",n_pages);
//Allocate the table for pages
pages = vzalloc(sizeof(* pages) * n_pages);
printk(KERN_ALERT "pages: %p",pages);
if(pages == NULL) {
res = -ENOMEM;
goto sglm_err1;
}
//Now pin the pages
res = get_user_pages_fast(((unsigned long)buf & PAGE_MASK), n_pages, 0, pages);
printk(KERN_ALERT "gupf: %d",res);
if(res < n_pages) {
int i;
for(i=0; i<res; i++)
put_page(pages[i]);
res = -ENOMEM;
goto sglm_err1;
}
//Now create the sg-list
res = sg_alloc_table_from_pages(sgt, pages, n_pages, offset, count, GFP_KERNEL);
printk(KERN_ALERT "satf: %d",res);
if(res < 0)
goto sglm_err2;
*a_pages = pages;
*a_n_pages = n_pages;
return res;
sglm_err2:
//Here we jump if we know that the pages are pinned
{
int i;
for(i=0; i<n_pages; i++)
put_page(pages[i]);
}
sglm_err1:
if(sgt) sg_free_table(sgt);
if(pages) kfree(pages);
* a_pages = NULL;
* a_n_pages = 0;
return res;
}
void sgt_destroy(struct sg_table * sgt, struct page ** pages, int n_pages)
{
int i;
//Free the sg list
if(sgt->sgl)
sg_free_table(sgt);
//Unpin pages
for(i=0; i < n_pages; i++) {
set_page_dirty(pages[i]);
put_page(pages[i]);
}
}
The sgt_prepare function builds the sg_table sgt structure that i can use to create the DMA mapping. I have verified that it contains the number of entries equal to the number of hugepages used.
Unfortunately, it requires that the list of the pages is created (allocated and returned via the a_pages pointer argument), and kept as long as the buffer is used.
Therefore, I really dislike that solution. Now I have 256 2MB hugepages used as a DMA buffer. It means that I have to create and keeep unnecessary 128*1024 page structures. I also waste 512 MB of kernel address space for unnecessary kernel mapping.
The interesting question is if the a_pages may be kept only temporarily (until the sg-list is created)? In theory it should be possible, as the pages are still locked...

Was: How does BPF calculate number of CPU for PERCPU_ARRAY?

I have encountered an interesting issue where a PERCPU_ARRAY created on one system with 2 processors creates an array with 2 per-CPU elements and on another system with 2 processors, an array with 128 per-CPU elements. The latter was rather unexpected to me!
The way I discovered this behavior is that a program that allocated an array for the number of CPUs (using get_nprocs_conf(3)) and then read in the PERCPU_ARRAY into it (using bpf_map_lookup_elem()) ended up writing past the end of the array and crashing.
I would like to find out what is the proper way to determine in a program that reads BPF maps the number of elements in a PERCPU_ARRAY used on a system.
Failing that, I think the second best approach is to pick a buffer for reading in that is "large enough." Here, the problem is similar: what is that number and is there way to learn it at runtime?
The question comes from reading the source of bpftool, which figures this out:
unsigned int get_possible_cpus(void)
{
int cpus = libbpf_num_possible_cpus();
if (cpus < 0) {
p_err("Can't get # of possible cpus: %s", strerror(-cpus));
exit(-1);
}
return cpus;
}
int libbpf_num_possible_cpus(void)
{
static const char *fcpu = "/sys/devices/system/cpu/possible";
static int cpus;
int err, n, i, tmp_cpus;
bool *mask;
/* ---8<--- snip */
}
So that's how they do it!

What does maximum resident set size mean?

#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <unistd.h>
int main() {
int i = 0;
struct rusage r_usage;
while (++i <= 10) {
void *m = malloc(20*1024*1024);
memset(m,0,20*1024*1024);
getrusage(RUSAGE_SELF,&r_usage);
printf("Memory usage = %ld\n",r_usage.ru_maxrss);
sleep (3);
}
printf("\nAllocated memory, sleeping ten seconds after which we will check again...\n\n");
sleep (10);
getrusage(RUSAGE_SELF,&r_usage);
printf("Memory usage = %ld\n",r_usage.ru_maxrss);
return 0;
}
The above code uses ru_maxrss attribute of rusage structure. It gives the value of maximum resident set size. What does it mean? Each time the program is executed it gives a different value. So please explain the output of this code?
These are screenshots of two executions of the same code which give different outputs, how can these numbers be explained or what can be interpreted from these two outputs?
Resident set size (RSS) means, roughly, the total amount of physical memory assigned to a process at a given point in time. It does not count pages that have been swapped out, or that are mapped from a file but not currently loaded into physical memory.
"Maximum RSS" means the maximum of the RSS since the process's birth, i.e. the largest it has ever been. So this number tells you the largest amount of physical memory your process has ever been using at any one instant.
It can vary from one run to the next if, for instance, the OS decided to swap out different amounts of your program's memory at different times. This decision would depend in part on what the rest of the system is doing, and where else physical memory is needed.

why it's slowly when I parse a message of Google protocol buffer in multi-thread?

I try to parse many Google protocol buffer messages from a binary file generated by calling SerializeToString. I first load all Bytes into a heap memory by calling new function. I also have two arrays to store the Bytes begin address of a message in the heap memory and the Bytes count of the message.
Then I begin to parse message by calling ParseFromString.I want to quicken the procedure by using multi-thread.
In each thread, I pass the start index and end index of address array and Byte count array.
In parent process. the main code is:
struct ParsePara
{
char* str_buffer;
size_t* buffer_offset;
size_t* binary_string_length_array;
size_t start_idx;
size_t end_idx;
Flight_Ticket_Info* ticket_info_buffer_array;
};
//Flight_Ticket_Info is class of message
//offset_size is the count of message
ticket_array = new Flight_Ticket_Info[offset_size];
const int max_thread_count = 6;
pthread_t pthread_id_vec[max_thread_count];
CTimer thread_cost;
thread_cost.start();
vector<ParsePara*> para_vec;
const size_t each_count = ceil(float(offset_size) / max_thread_count);
for (size_t k = 0;k < max_thread_count;k++)
{
size_t start_idx = each_count * k;
size_t end_idx = each_count * (k+1);
if (start_idx >= offset_size)
break;
if (end_idx >= offset_size)
end_idx = offset_size;
ParsePara* cand_para_ptr = new ParsePara();
if (!cand_para_ptr)
{
_ERROR_EXIT(0,"[Malloc memory fail.]");
}
cand_para_ptr->str_buffer = m_valdata;//heap memory for storing Bytes of message
cand_para_ptr->buffer_offset = offset_array;//begin address of each message
cand_para_ptr->start_idx = start_idx;
cand_para_ptr->end_idx = end_idx;
cand_para_ptr->ticket_info_buffer_array = ticket_array;//array to store message
cand_para_ptr->binary_string_length_array = binary_length_array;//Bytes count of each message
para_vec.push_back(cand_para_ptr);
}
for(size_t k = 0 ;k < para_vec.size();k++)
{
int ret = pthread_create(&pthread_id_vec[k],NULL,parserFlightTicketForMultiThread,para_vec[k]);
if (0 != ret)
{
_ERROR_EXIT(0,"[Error] [create thread fail]");
}
}
for (size_t k = 0;k < para_vec.size();k++)
{
pthread_join(pthread_id_vec[k],NULL);
}
In each thread the thread function is:
void* parserFlightTicketForMultiThread(void* void_para_ptr)
{
ParsePara* para_ptr = (ParsePara*) void_para_ptr;
parserFlightTicketForMany(para_ptr->str_buffer,para_ptr->ticket_info_buffer_array,para_ptr->buffer_offset,
para_ptr->start_idx,para_ptr->end_idx,para_ptr->binary_string_length_array);
}
void parserFlightTicketForMany(const char* str_buffer,Flight_Ticket_Info* ticket_info_buffer_array,
size_t* buffer_offset,const size_t start_idx,const size_t end_idx,size_t* binary_string_length_array)
{
printf("start_idx:%d,end_idx:%d\n",start_idx,end_idx);
for (size_t k = start_idx;k < end_idx;k++)
{
if (k % 100000 == 0)
cout << k << endl;
size_t cand_offset = buffer_offset[k];
size_t binary_length = binary_string_length_array[k];
ticket_info_buffer_array[k].ParseFromString(string(&str_buffer[cand_offset],binary_length-1));
}
printf("done %ld %ld\n",start_idx,end_idx);
}
But multi-thread cost is more than one thread.
one thread cost is:40455623ms
My computer is 8 core and six thread cost is:131586865ms
Anyone can help me? thank you!
Some possible problems -- you'll have to experiment to determine which:
Protobuf parsing speed is often limited by memory bandwidth rather than CPU time, especially with a large input data set. In that case, more threads won't help, since all the cores are sharing bandwidth to main memory. Indeed, having multiple cores fighting over memory bandwidth could make the overall operation slower. Note that the biggest consumer of memory is not the input bytes but rather the parsed data objects -- that is, the output of parsing -- which are many times larger than the encoded data. To improve this problem, consider writing the parsing loop so that it fully-processes each message immediately after parsing, before moving on to the text message. That way, instead of allocating k protobuf objects, you only need to allocate one protobuf object per thread, and repeatedly reuse the same object for parsing. This way the object will (probably) stay in the core's private L1 cache and avoid consuming memory bandwidth; only the input bytes will be read over the main bus.
How are you loading data into RAM? Did you read() into a large array or did you mmap()? In the latter case the data is read from disk lazily -- it won't happen until you actually attempt to parse it. Even in the read() case, it could be that the data has been swapped out, creating similar effects. Either way, your threads are now not just fighting for memory bandwidth, but disk bandwidth, which is of course much slower. Having six threads reading separate parts of a big file will definitely be slower overall than having one thread read the whole file, because the operating system optimizes for sequential access.
Protobuf allocates memory during parsing. Many memory allocators take a lock while allocating new memory. Since all your threads are allocating tons and tons of objects in a tight loop, they will contend for this lock. Make sure you are using a thread-friendly memory allocator, such as Google's tcmalloc. Note that repeatedly reusing the same protobuf object in a parse-consume loop rather than allocating lots of different objects will also help immensely here, because the protobuf object will automatically reuse memory for sub-objects.
There may be a bug in your code and it might not be doing what you expect at all when multithreaded. For example, a bug might be causing all the threads to process the same data, rather than different data, and it could be that the data they're choosing happens to be bigger. Make sure you are testing that the results of your code are exactly the same when you run single-threaded vs. multi-threaded.
In short, if you want multiple cores to make your code faster, you have to think about not just what each core is doing, but what data is going in and out of each core, and how much the cores have to talk to each other. Ideally you want each core to operate all on its own without talking to anyone or anything; then you get maximum parallelism. That's not usually possible, of course, but the closer you can get to that, the better.
BTW, a random optimization for you:
ParseFromString(string(&str_buffer[cand_offset],binary_length-1))
Replace that with:
ParseFromArray(&str_buffer[cand_offset],binary_length-1)
Creating at std::string makes a copy of the data, which wastes time (and memory bandwidth). (This doesn't explain why threading is slow, though.)

Confusing result from counting page fault in linux

I was writing programs to count the time of page faults in a linux system. More precisely, the time kernel execute the function __do_page_fault.
And somehow I wrote two global variables, named pfcount_at_beg and pfcount_at_end, which increase once when the function __do_page_fault is executed at different locations of the function.
To illustrate, the modified function goes as:
unsigned long pfcount_at_beg = 0;
unsigned long pfcount_at_end = 0;
static void __kprobes
__do_page_fault(...)
{
struct vm_area_sruct *vma;
... // VARIABLES DEFINITION
unsigned int flags = FAULT_FLAG_ALLOW_RETRY | FAULT_FLAG_KILLABLE;
pfcount_at_beg++; // I add THIS
...
...
// ORIGINAL CODE OF THE FUNCTION
...
pfcount_at_end++; // I add THIS
}
I expected that the value of pfcount_at_end is smaller than the value of pfcount_at_beg.
Because, I think, every time kernel executes the instructions of code pfcount_at_end++, it must have executed pfcount_at_beg++(Every function starts at the very beginning of the code).
On the other hand, as there are many conditional return between these two lines of code.
However, the result turns out oppositely. The value of pfcount_at_end is larger than the value of pfcount_at_beg.
I use printk to print these kernel variables through a self-defined syscall. And I wrote the user level program to call the system call.
Here is my simple syscall and user-level program:
// syscall
asmlinkage int sys_mysyscall(void)
{
printk( KERN_INFO "total pf_at_beg%lu\ntotal pf_at_end%lu\n", pfcount_at_beg, pfcount_at_end)
return 0;
}
// user-level program
#include<linux/unistd.h>
#include<sys/syscall.h>
#define __NR_mysyscall 223
int main()
{
syscall(__NR_mysyscall);
return 0;
}
Is there anybody who knows what exactly happened during this?
Just now I modified the code, to make pfcount_at_beg and pfcount_at_end static. However the result did not change, i.e. the value of pfcount_at_end is larger than the value of pfcount_at_beg.
So possibly it might be caused by in-atomic operation of increment. Would it be better if I use read-write lock?
The ++ operator is not garanteed to be atomic, so your counters may suffer concurrent access and have incorrect values. You should protect your increment as a critical section, or use the atomic_t type defined in <asm/atomic.h>, and its related atomic_set() and atomic_add() functions (and a lot more).
Not directly connected to your issue, but using a specific syscall is overkill (but maybe it is an exercise). A lighter solution could be to use a /proc entry (also an interesting exercise).

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