linux/lib/dynamic_queue_limits.c

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License cleanup: add SPDX GPL-2.0 license identifier to files with no license Many source files in the tree are missing licensing information, which makes it harder for compliance tools to determine the correct license. By default all files without license information are under the default license of the kernel, which is GPL version 2. Update the files which contain no license information with the 'GPL-2.0' SPDX license identifier. The SPDX identifier is a legally binding shorthand, which can be used instead of the full boiler plate text. This patch is based on work done by Thomas Gleixner and Kate Stewart and Philippe Ombredanne. How this work was done: Patches were generated and checked against linux-4.14-rc6 for a subset of the use cases: - file had no licensing information it it. - file was a */uapi/* one with no licensing information in it, - file was a */uapi/* one with existing licensing information, Further patches will be generated in subsequent months to fix up cases where non-standard license headers were used, and references to license had to be inferred by heuristics based on keywords. The analysis to determine which SPDX License Identifier to be applied to a file was done in a spreadsheet of side by side results from of the output of two independent scanners (ScanCode & Windriver) producing SPDX tag:value files created by Philippe Ombredanne. Philippe prepared the base worksheet, and did an initial spot review of a few 1000 files. The 4.13 kernel was the starting point of the analysis with 60,537 files assessed. Kate Stewart did a file by file comparison of the scanner results in the spreadsheet to determine which SPDX license identifier(s) to be applied to the file. She confirmed any determination that was not immediately clear with lawyers working with the Linux Foundation. Criteria used to select files for SPDX license identifier tagging was: - Files considered eligible had to be source code files. - Make and config files were included as candidates if they contained >5 lines of source - File already had some variant of a license header in it (even if <5 lines). All documentation files were explicitly excluded. The following heuristics were used to determine which SPDX license identifiers to apply. - when both scanners couldn't find any license traces, file was considered to have no license information in it, and the top level COPYING file license applied. For non */uapi/* files that summary was: SPDX license identifier # files ---------------------------------------------------|------- GPL-2.0 11139 and resulted in the first patch in this series. If that file was a */uapi/* path one, it was "GPL-2.0 WITH Linux-syscall-note" otherwise it was "GPL-2.0". Results of that was: SPDX license identifier # files ---------------------------------------------------|------- GPL-2.0 WITH Linux-syscall-note 930 and resulted in the second patch in this series. - if a file had some form of licensing information in it, and was one of the */uapi/* ones, it was denoted with the Linux-syscall-note if any GPL family license was found in the file or had no licensing in it (per prior point). Results summary: SPDX license identifier # files ---------------------------------------------------|------ GPL-2.0 WITH Linux-syscall-note 270 GPL-2.0+ WITH Linux-syscall-note 169 ((GPL-2.0 WITH Linux-syscall-note) OR BSD-2-Clause) 21 ((GPL-2.0 WITH Linux-syscall-note) OR BSD-3-Clause) 17 LGPL-2.1+ WITH Linux-syscall-note 15 GPL-1.0+ WITH Linux-syscall-note 14 ((GPL-2.0+ WITH Linux-syscall-note) OR BSD-3-Clause) 5 LGPL-2.0+ WITH Linux-syscall-note 4 LGPL-2.1 WITH Linux-syscall-note 3 ((GPL-2.0 WITH Linux-syscall-note) OR MIT) 3 ((GPL-2.0 WITH Linux-syscall-note) AND MIT) 1 and that resulted in the third patch in this series. - when the two scanners agreed on the detected license(s), that became the concluded license(s). - when there was disagreement between the two scanners (one detected a license but the other didn't, or they both detected different licenses) a manual inspection of the file occurred. - In most cases a manual inspection of the information in the file resulted in a clear resolution of the license that should apply (and which scanner probably needed to revisit its heuristics). - When it was not immediately clear, the license identifier was confirmed with lawyers working with the Linux Foundation. - If there was any question as to the appropriate license identifier, the file was flagged for further research and to be revisited later in time. In total, over 70 hours of logged manual review was done on the spreadsheet to determine the SPDX license identifiers to apply to the source files by Kate, Philippe, Thomas and, in some cases, confirmation by lawyers working with the Linux Foundation. Kate also obtained a third independent scan of the 4.13 code base from FOSSology, and compared selected files where the other two scanners disagreed against that SPDX file, to see if there was new insights. The Windriver scanner is based on an older version of FOSSology in part, so they are related. Thomas did random spot checks in about 500 files from the spreadsheets for the uapi headers and agreed with SPDX license identifier in the files he inspected. For the non-uapi files Thomas did random spot checks in about 15000 files. In initial set of patches against 4.14-rc6, 3 files were found to have copy/paste license identifier errors, and have been fixed to reflect the correct identifier. Additionally Philippe spent 10 hours this week doing a detailed manual inspection and review of the 12,461 patched files from the initial patch version early this week with: - a full scancode scan run, collecting the matched texts, detected license ids and scores - reviewing anything where there was a license detected (about 500+ files) to ensure that the applied SPDX license was correct - reviewing anything where there was no detection but the patch license was not GPL-2.0 WITH Linux-syscall-note to ensure that the applied SPDX license was correct This produced a worksheet with 20 files needing minor correction. This worksheet was then exported into 3 different .csv files for the different types of files to be modified. These .csv files were then reviewed by Greg. Thomas wrote a script to parse the csv files and add the proper SPDX tag to the file, in the format that the file expected. This script was further refined by Greg based on the output to detect more types of files automatically and to distinguish between header and source .c files (which need different comment types.) Finally Greg ran the script using the .csv files to generate the patches. Reviewed-by: Kate Stewart <kstewart@linuxfoundation.org> Reviewed-by: Philippe Ombredanne <pombredanne@nexb.com> Reviewed-by: Thomas Gleixner <tglx@linutronix.de> Signed-off-by: Greg Kroah-Hartman <gregkh@linuxfoundation.org>
2017-11-01 14:07:57 +00:00
// SPDX-License-Identifier: GPL-2.0
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
/*
* Dynamic byte queue limits. See include/linux/dynamic_queue_limits.h
*
* Copyright (c) 2011, Tom Herbert <therbert@google.com>
*/
#include <linux/types.h>
#include <linux/kernel.h>
#include <linux/jiffies.h>
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
#include <linux/dynamic_queue_limits.h>
#include <linux/compiler.h>
#include <linux/export.h>
net: dqs: add NIC stall detector based on BQL softnet_data->time_squeeze is sometimes used as a proxy for host overload or indication of scheduling problems. In practice this statistic is very noisy and has hard to grasp units - e.g. is 10 squeezes a second to be expected, or high? Delaying network (NAPI) processing leads to drops on NIC queues but also RTT bloat, impacting pacing and CA decisions. Stalls are a little hard to detect on the Rx side, because there may simply have not been any packets received in given period of time. Packet timestamps help a little bit, but again we don't know if packets are stale because we're not keeping up or because someone (*cough* cgroups) disabled IRQs for a long time. We can, however, use Tx as a proxy for Rx stalls. Most drivers use combined Rx+Tx NAPIs so if Tx gets starved so will Rx. On the Tx side we know exactly when packets get queued, and completed, so there is no uncertainty. This patch adds stall checks to BQL. Why BQL? Because it's a convenient place to add such checks, already called by most drivers, and it has copious free space in its structures (this patch adds no extra cache references or dirtying to the fast path). The algorithm takes one parameter - max delay AKA stall threshold and increments a counter whenever NAPI got delayed for at least that amount of time. It also records the length of the longest stall. To be precise every time NAPI has not polled for at least stall thrs we check if there were any Tx packets queued between last NAPI run and now - stall_thrs/2. Unlike the classic Tx watchdog this mechanism does not ignore stalls caused by Tx being disabled, or loss of link. I don't think the check is worth the complexity, and stall is a stall, whether due to host overload, flow control, link down... doesn't matter much to the application. We have been running this detector in production at Meta for 2 years, with the threshold of 8ms. It's the lowest value where false positives become rare. There's still a constant stream of reported stalls (especially without the ksoftirqd deferral patches reverted), those who like their stall metrics to be 0 may prefer higher value. Signed-off-by: Jakub Kicinski <kuba@kernel.org> Signed-off-by: Breno Leitao <leitao@debian.org> Signed-off-by: David S. Miller <davem@davemloft.net>
2024-03-04 14:08:47 +00:00
#include <trace/events/napi.h>
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
#define POSDIFF(A, B) ((int)((A) - (B)) > 0 ? (A) - (B) : 0)
#define AFTER_EQ(A, B) ((int)((A) - (B)) >= 0)
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
static void dql_check_stall(struct dql *dql, unsigned short stall_thrs)
net: dqs: add NIC stall detector based on BQL softnet_data->time_squeeze is sometimes used as a proxy for host overload or indication of scheduling problems. In practice this statistic is very noisy and has hard to grasp units - e.g. is 10 squeezes a second to be expected, or high? Delaying network (NAPI) processing leads to drops on NIC queues but also RTT bloat, impacting pacing and CA decisions. Stalls are a little hard to detect on the Rx side, because there may simply have not been any packets received in given period of time. Packet timestamps help a little bit, but again we don't know if packets are stale because we're not keeping up or because someone (*cough* cgroups) disabled IRQs for a long time. We can, however, use Tx as a proxy for Rx stalls. Most drivers use combined Rx+Tx NAPIs so if Tx gets starved so will Rx. On the Tx side we know exactly when packets get queued, and completed, so there is no uncertainty. This patch adds stall checks to BQL. Why BQL? Because it's a convenient place to add such checks, already called by most drivers, and it has copious free space in its structures (this patch adds no extra cache references or dirtying to the fast path). The algorithm takes one parameter - max delay AKA stall threshold and increments a counter whenever NAPI got delayed for at least that amount of time. It also records the length of the longest stall. To be precise every time NAPI has not polled for at least stall thrs we check if there were any Tx packets queued between last NAPI run and now - stall_thrs/2. Unlike the classic Tx watchdog this mechanism does not ignore stalls caused by Tx being disabled, or loss of link. I don't think the check is worth the complexity, and stall is a stall, whether due to host overload, flow control, link down... doesn't matter much to the application. We have been running this detector in production at Meta for 2 years, with the threshold of 8ms. It's the lowest value where false positives become rare. There's still a constant stream of reported stalls (especially without the ksoftirqd deferral patches reverted), those who like their stall metrics to be 0 may prefer higher value. Signed-off-by: Jakub Kicinski <kuba@kernel.org> Signed-off-by: Breno Leitao <leitao@debian.org> Signed-off-by: David S. Miller <davem@davemloft.net>
2024-03-04 14:08:47 +00:00
{
unsigned long now;
if (!stall_thrs)
return;
now = jiffies;
/* Check for a potential stall */
if (time_after_eq(now, dql->last_reap + stall_thrs)) {
unsigned long hist_head, t, start, end;
/* We are trying to detect a period of at least @stall_thrs
* jiffies without any Tx completions, but during first half
* of which some Tx was posted.
*/
dqs_again:
hist_head = READ_ONCE(dql->history_head);
/* pairs with smp_wmb() in dql_queued() */
smp_rmb();
/* Get the previous entry in the ring buffer, which is the
* oldest sample.
*/
start = (hist_head - DQL_HIST_LEN + 1) * BITS_PER_LONG;
/* Advance start to continue from the last reap time */
if (time_before(start, dql->last_reap + 1))
start = dql->last_reap + 1;
/* Newest sample we should have already seen a completion for */
end = hist_head * BITS_PER_LONG + (BITS_PER_LONG - 1);
/* Shrink the search space to [start, (now - start_thrs/2)] if
* `end` is beyond the stall zone
*/
if (time_before(now, end + stall_thrs / 2))
end = now - stall_thrs / 2;
/* Search for the queued time in [t, end] */
for (t = start; time_before_eq(t, end); t++)
if (test_bit(t % (DQL_HIST_LEN * BITS_PER_LONG),
dql->history))
break;
/* Variable t contains the time of the queue */
if (!time_before_eq(t, end))
goto no_stall;
/* The ring buffer was modified in the meantime, retry */
if (hist_head != READ_ONCE(dql->history_head))
goto dqs_again;
dql->stall_cnt++;
dql->stall_max = max_t(unsigned short, dql->stall_max, now - t);
trace_dql_stall_detected(dql->stall_thrs, now - t,
dql->last_reap, dql->history_head,
now, dql->history);
}
no_stall:
dql->last_reap = now;
}
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
/* Records completed count and recalculates the queue limit */
void dql_completed(struct dql *dql, unsigned int count)
{
unsigned int inprogress, prev_inprogress, limit;
unsigned int ovlimit, completed, num_queued;
unsigned short stall_thrs;
bool all_prev_completed;
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
locking/atomics: COCCINELLE/treewide: Convert trivial ACCESS_ONCE() patterns to READ_ONCE()/WRITE_ONCE() Please do not apply this to mainline directly, instead please re-run the coccinelle script shown below and apply its output. For several reasons, it is desirable to use {READ,WRITE}_ONCE() in preference to ACCESS_ONCE(), and new code is expected to use one of the former. So far, there's been no reason to change most existing uses of ACCESS_ONCE(), as these aren't harmful, and changing them results in churn. However, for some features, the read/write distinction is critical to correct operation. To distinguish these cases, separate read/write accessors must be used. This patch migrates (most) remaining ACCESS_ONCE() instances to {READ,WRITE}_ONCE(), using the following coccinelle script: ---- // Convert trivial ACCESS_ONCE() uses to equivalent READ_ONCE() and // WRITE_ONCE() // $ make coccicheck COCCI=/home/mark/once.cocci SPFLAGS="--include-headers" MODE=patch virtual patch @ depends on patch @ expression E1, E2; @@ - ACCESS_ONCE(E1) = E2 + WRITE_ONCE(E1, E2) @ depends on patch @ expression E; @@ - ACCESS_ONCE(E) + READ_ONCE(E) ---- Signed-off-by: Mark Rutland <mark.rutland@arm.com> Signed-off-by: Paul E. McKenney <paulmck@linux.vnet.ibm.com> Cc: Linus Torvalds <torvalds@linux-foundation.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Thomas Gleixner <tglx@linutronix.de> Cc: davem@davemloft.net Cc: linux-arch@vger.kernel.org Cc: mpe@ellerman.id.au Cc: shuah@kernel.org Cc: snitzer@redhat.com Cc: thor.thayer@linux.intel.com Cc: tj@kernel.org Cc: viro@zeniv.linux.org.uk Cc: will.deacon@arm.com Link: http://lkml.kernel.org/r/1508792849-3115-19-git-send-email-paulmck@linux.vnet.ibm.com Signed-off-by: Ingo Molnar <mingo@kernel.org>
2017-10-23 21:07:29 +00:00
num_queued = READ_ONCE(dql->num_queued);
/* Read stall_thrs in advance since it belongs to the same (first)
* cache line as ->num_queued. This way, dql_check_stall() does not
* need to touch the first cache line again later, reducing the window
* of possible false sharing.
*/
stall_thrs = READ_ONCE(dql->stall_thrs);
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
/* Can't complete more than what's in queue */
BUG_ON(count > num_queued - dql->num_completed);
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
completed = dql->num_completed + count;
limit = dql->limit;
ovlimit = POSDIFF(num_queued - dql->num_completed, limit);
inprogress = num_queued - completed;
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
prev_inprogress = dql->prev_num_queued - dql->num_completed;
all_prev_completed = AFTER_EQ(completed, dql->prev_num_queued);
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
if ((ovlimit && !inprogress) ||
(dql->prev_ovlimit && all_prev_completed)) {
/*
* Queue considered starved if:
* - The queue was over-limit in the last interval,
* and there is no more data in the queue.
* OR
* - The queue was over-limit in the previous interval and
* when enqueuing it was possible that all queued data
* had been consumed. This covers the case when queue
* may have becomes starved between completion processing
* running and next time enqueue was scheduled.
*
* When queue is starved increase the limit by the amount
* of bytes both sent and completed in the last interval,
* plus any previous over-limit.
*/
limit += POSDIFF(completed, dql->prev_num_queued) +
dql->prev_ovlimit;
dql->slack_start_time = jiffies;
dql->lowest_slack = UINT_MAX;
} else if (inprogress && prev_inprogress && !all_prev_completed) {
/*
* Queue was not starved, check if the limit can be decreased.
* A decrease is only considered if the queue has been busy in
* the whole interval (the check above).
*
* If there is slack, the amount of excess data queued above
* the amount needed to prevent starvation, the queue limit
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
* can be decreased. To avoid hysteresis we consider the
* minimum amount of slack found over several iterations of the
* completion routine.
*/
unsigned int slack, slack_last_objs;
/*
* Slack is the maximum of
* - The queue limit plus previous over-limit minus twice
* the number of objects completed. Note that two times
* number of completed bytes is a basis for an upper bound
* of the limit.
* - Portion of objects in the last queuing operation that
* was not part of non-zero previous over-limit. That is
* "round down" by non-overlimit portion of the last
* queueing operation.
*/
slack = POSDIFF(limit + dql->prev_ovlimit,
2 * (completed - dql->num_completed));
slack_last_objs = dql->prev_ovlimit ?
POSDIFF(dql->prev_last_obj_cnt, dql->prev_ovlimit) : 0;
slack = max(slack, slack_last_objs);
if (slack < dql->lowest_slack)
dql->lowest_slack = slack;
if (time_after(jiffies,
dql->slack_start_time + dql->slack_hold_time)) {
limit = POSDIFF(limit, dql->lowest_slack);
dql->slack_start_time = jiffies;
dql->lowest_slack = UINT_MAX;
}
}
/* Enforce bounds on limit */
limit = clamp(limit, dql->min_limit, dql->max_limit);
if (limit != dql->limit) {
dql->limit = limit;
ovlimit = 0;
}
dql->adj_limit = limit + completed;
dql->prev_ovlimit = ovlimit;
dql->prev_last_obj_cnt = dql->last_obj_cnt;
dql->num_completed = completed;
dql->prev_num_queued = num_queued;
net: dqs: add NIC stall detector based on BQL softnet_data->time_squeeze is sometimes used as a proxy for host overload or indication of scheduling problems. In practice this statistic is very noisy and has hard to grasp units - e.g. is 10 squeezes a second to be expected, or high? Delaying network (NAPI) processing leads to drops on NIC queues but also RTT bloat, impacting pacing and CA decisions. Stalls are a little hard to detect on the Rx side, because there may simply have not been any packets received in given period of time. Packet timestamps help a little bit, but again we don't know if packets are stale because we're not keeping up or because someone (*cough* cgroups) disabled IRQs for a long time. We can, however, use Tx as a proxy for Rx stalls. Most drivers use combined Rx+Tx NAPIs so if Tx gets starved so will Rx. On the Tx side we know exactly when packets get queued, and completed, so there is no uncertainty. This patch adds stall checks to BQL. Why BQL? Because it's a convenient place to add such checks, already called by most drivers, and it has copious free space in its structures (this patch adds no extra cache references or dirtying to the fast path). The algorithm takes one parameter - max delay AKA stall threshold and increments a counter whenever NAPI got delayed for at least that amount of time. It also records the length of the longest stall. To be precise every time NAPI has not polled for at least stall thrs we check if there were any Tx packets queued between last NAPI run and now - stall_thrs/2. Unlike the classic Tx watchdog this mechanism does not ignore stalls caused by Tx being disabled, or loss of link. I don't think the check is worth the complexity, and stall is a stall, whether due to host overload, flow control, link down... doesn't matter much to the application. We have been running this detector in production at Meta for 2 years, with the threshold of 8ms. It's the lowest value where false positives become rare. There's still a constant stream of reported stalls (especially without the ksoftirqd deferral patches reverted), those who like their stall metrics to be 0 may prefer higher value. Signed-off-by: Jakub Kicinski <kuba@kernel.org> Signed-off-by: Breno Leitao <leitao@debian.org> Signed-off-by: David S. Miller <davem@davemloft.net>
2024-03-04 14:08:47 +00:00
dql_check_stall(dql, stall_thrs);
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
}
EXPORT_SYMBOL(dql_completed);
void dql_reset(struct dql *dql)
{
/* Reset all dynamic values */
dql->limit = 0;
dql->num_queued = 0;
dql->num_completed = 0;
dql->last_obj_cnt = 0;
dql->prev_num_queued = 0;
dql->prev_last_obj_cnt = 0;
dql->prev_ovlimit = 0;
dql->lowest_slack = UINT_MAX;
dql->slack_start_time = jiffies;
net: dqs: add NIC stall detector based on BQL softnet_data->time_squeeze is sometimes used as a proxy for host overload or indication of scheduling problems. In practice this statistic is very noisy and has hard to grasp units - e.g. is 10 squeezes a second to be expected, or high? Delaying network (NAPI) processing leads to drops on NIC queues but also RTT bloat, impacting pacing and CA decisions. Stalls are a little hard to detect on the Rx side, because there may simply have not been any packets received in given period of time. Packet timestamps help a little bit, but again we don't know if packets are stale because we're not keeping up or because someone (*cough* cgroups) disabled IRQs for a long time. We can, however, use Tx as a proxy for Rx stalls. Most drivers use combined Rx+Tx NAPIs so if Tx gets starved so will Rx. On the Tx side we know exactly when packets get queued, and completed, so there is no uncertainty. This patch adds stall checks to BQL. Why BQL? Because it's a convenient place to add such checks, already called by most drivers, and it has copious free space in its structures (this patch adds no extra cache references or dirtying to the fast path). The algorithm takes one parameter - max delay AKA stall threshold and increments a counter whenever NAPI got delayed for at least that amount of time. It also records the length of the longest stall. To be precise every time NAPI has not polled for at least stall thrs we check if there were any Tx packets queued between last NAPI run and now - stall_thrs/2. Unlike the classic Tx watchdog this mechanism does not ignore stalls caused by Tx being disabled, or loss of link. I don't think the check is worth the complexity, and stall is a stall, whether due to host overload, flow control, link down... doesn't matter much to the application. We have been running this detector in production at Meta for 2 years, with the threshold of 8ms. It's the lowest value where false positives become rare. There's still a constant stream of reported stalls (especially without the ksoftirqd deferral patches reverted), those who like their stall metrics to be 0 may prefer higher value. Signed-off-by: Jakub Kicinski <kuba@kernel.org> Signed-off-by: Breno Leitao <leitao@debian.org> Signed-off-by: David S. Miller <davem@davemloft.net>
2024-03-04 14:08:47 +00:00
dql->last_reap = jiffies;
dql->history_head = jiffies / BITS_PER_LONG;
memset(dql->history, 0, sizeof(dql->history));
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
}
EXPORT_SYMBOL(dql_reset);
void dql_init(struct dql *dql, unsigned int hold_time)
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
{
dql->max_limit = DQL_MAX_LIMIT;
dql->min_limit = 0;
dql->slack_hold_time = hold_time;
net: dqs: add NIC stall detector based on BQL softnet_data->time_squeeze is sometimes used as a proxy for host overload or indication of scheduling problems. In practice this statistic is very noisy and has hard to grasp units - e.g. is 10 squeezes a second to be expected, or high? Delaying network (NAPI) processing leads to drops on NIC queues but also RTT bloat, impacting pacing and CA decisions. Stalls are a little hard to detect on the Rx side, because there may simply have not been any packets received in given period of time. Packet timestamps help a little bit, but again we don't know if packets are stale because we're not keeping up or because someone (*cough* cgroups) disabled IRQs for a long time. We can, however, use Tx as a proxy for Rx stalls. Most drivers use combined Rx+Tx NAPIs so if Tx gets starved so will Rx. On the Tx side we know exactly when packets get queued, and completed, so there is no uncertainty. This patch adds stall checks to BQL. Why BQL? Because it's a convenient place to add such checks, already called by most drivers, and it has copious free space in its structures (this patch adds no extra cache references or dirtying to the fast path). The algorithm takes one parameter - max delay AKA stall threshold and increments a counter whenever NAPI got delayed for at least that amount of time. It also records the length of the longest stall. To be precise every time NAPI has not polled for at least stall thrs we check if there were any Tx packets queued between last NAPI run and now - stall_thrs/2. Unlike the classic Tx watchdog this mechanism does not ignore stalls caused by Tx being disabled, or loss of link. I don't think the check is worth the complexity, and stall is a stall, whether due to host overload, flow control, link down... doesn't matter much to the application. We have been running this detector in production at Meta for 2 years, with the threshold of 8ms. It's the lowest value where false positives become rare. There's still a constant stream of reported stalls (especially without the ksoftirqd deferral patches reverted), those who like their stall metrics to be 0 may prefer higher value. Signed-off-by: Jakub Kicinski <kuba@kernel.org> Signed-off-by: Breno Leitao <leitao@debian.org> Signed-off-by: David S. Miller <davem@davemloft.net>
2024-03-04 14:08:47 +00:00
dql->stall_thrs = 0;
dql: Dynamic queue limits Implementation of dynamic queue limits (dql). This is a libary which allows a queue limit to be dynamically managed. The goal of dql is to set the queue limit, number of objects to the queue, to be minimized without allowing the queue to be starved. dql would be used with a queue which has these properties: 1) Objects are queued up to some limit which can be expressed as a count of objects. 2) Periodically a completion process executes which retires consumed objects. 3) Starvation occurs when limit has been reached, all queued data has actually been consumed but completion processing has not yet run, so queuing new data is blocked. 4) Minimizing the amount of queued data is desirable. A canonical example of such a queue would be a NIC HW transmit queue. The queue limit is dynamic, it will increase or decrease over time depending on the workload. The queue limit is recalculated each time completion processing is done. Increases occur when the queue is starved and can exponentially increase over successive intervals. Decreases occur when more data is being maintained in the queue than needed to prevent starvation. The number of extra objects, or "slack", is measured over successive intervals, and to avoid hysteresis the limit is only reduced by the miminum slack seen over a configurable time period. dql API provides routines to manage the queue: - dql_init is called to intialize the dql structure - dql_reset is called to reset dynamic values - dql_queued called when objects are being enqueued - dql_avail returns availability in the queue - dql_completed is called when objects have be consumed in the queue Configuration consists of: - max_limit, maximum limit - min_limit, minimum limit - slack_hold_time, time to measure instances of slack before reducing queue limit Signed-off-by: Tom Herbert <therbert@google.com> Acked-by: Eric Dumazet <eric.dumazet@gmail.com> Signed-off-by: David S. Miller <davem@davemloft.net>
2011-11-28 16:32:35 +00:00
dql_reset(dql);
}
EXPORT_SYMBOL(dql_init);