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LLVM-MCA(1)			     LLVM			   LLVM-MCA(1)

NAME
       llvm-mca	- LLVM Machine Code Analyzer

SYNOPSIS
       llvm-mca	[options] [input]

DESCRIPTION
       llvm-mca	is a performance analysis tool that uses information available
       in LLVM (e.g. scheduling	models)	to statically measure the  performance
       of machine code in a specific CPU.

       Performance is measured in terms	of throughput as well as processor re-
       source consumption. The tool currently works  for  processors  with  an
       out-of-order  backend,  for which there is a scheduling model available
       in LLVM.

       The main	goal of	this tool is not just to predict  the  performance  of
       the  code  when run on the target, but also help	with diagnosing	poten-
       tial performance	issues.

       Given an	assembly code sequence,	llvm-mca  estimates  the  Instructions
       Per  Cycle  (IPC),  as well as hardware resource	pressure. The analysis
       and reporting style were	inspired by the	IACA tool from Intel.

       For example, you	can compile code with clang, output assembly, and pipe
       it directly into	llvm-mca for analysis:

	  $ clang foo.c	-O2 -target x86_64-unknown-unknown -S -o - | llvm-mca -mcpu=btver2

       Or for Intel syntax:

	  $ clang foo.c	-O2 -target x86_64-unknown-unknown -mllvm -x86-asm-syntax=intel	-S -o -	| llvm-mca -mcpu=btver2

OPTIONS
       If  input is "-"	or omitted, llvm-mca reads from	standard input.	Other-
       wise, it	will read from the specified filename.

       If the -o option	is omitted, then llvm-mca  will	 send  its  output  to
       standard	 output	if the input is	from standard input.  If the -o	option
       specifies "-", then the output will also	be sent	to standard output.

       -help  Print a summary of command line options.

       -mtriple=<target	triple>
	      Specify a	target triple string.

       -march=<arch>
	      Specify the architecture for which to analyze the	code.  It  de-
	      faults to	the host default target.

       -mcpu=<cpuname>
	      Specify  the  processor  for  which to analyze the code.	By de-
	      fault, the cpu name is autodetected from the host.

       -output-asm-variant=<variant id>
	      Specify the output assembly variant for the report generated  by
	      the  tool.   On  x86,  possible  values are [0, 1]. A value of 0
	      (vic. 1) for this	flag enables the AT&T  (vic.  Intel)  assembly
	      format  for the code printed out by the tool in the analysis re-
	      port.

       -dispatch=<width>
	      Specify a	different dispatch width for the processor.  The  dis-
	      patch  width  defaults  to  field	 'IssueWidth' in the processor
	      scheduling model.	 If width is zero, then	the  default  dispatch
	      width is used.

       -register-file-size=<size>
	      Specify the size of the register file. When specified, this flag
	      limits how many physical registers are  available	 for  register
	      renaming	purposes.  A value of zero for this flag means "unlim-
	      ited number of physical registers".

       -iterations=<number of iterations>
	      Specify the number of iterations to run. If this flag is set  to
	      0,  then	the  tool  sets	 the number of iterations to a default
	      value (i.e. 100).

       -noalias=<bool>
	      If set, the tool assumes that loads and stores don't alias. This
	      is the default behavior.

       -lqueue=<load queue size>
	      Specify  the  size of the	load queue in the load/store unit emu-
	      lated by the tool.  By default, the tool assumes an unbound num-
	      ber of entries in	the load queue.	 A value of zero for this flag
	      is ignored, and the default load queue size is used instead.

       -squeue=<store queue size>
	      Specify the size of the store queue in the load/store unit  emu-
	      lated  by	the tool. By default, the tool assumes an unbound num-
	      ber of entries in	the store queue. A value of zero for this flag
	      is ignored, and the default store	queue size is used instead.

       -timeline
	      Enable the timeline view.

       -timeline-max-iterations=<iterations>
	      Limit the	number of iterations to	print in the timeline view. By
	      default, the timeline view prints	information for	up to 10 iter-
	      ations.

       -timeline-max-cycles=<cycles>
	      Limit the	number of cycles in the	timeline view. By default, the
	      number of	cycles is set to 80.

       -resource-pressure
	      Enable the resource pressure view. This is enabled by default.

       -register-file-stats
	      Enable register file usage statistics.

       -dispatch-stats
	      Enable extra dispatch statistics.	This view  collects  and  ana-
	      lyzes  instruction  dispatch  events,  as	well as	static/dynamic
	      dispatch stall events. This view is disabled by default.

       -scheduler-stats
	      Enable extra scheduler statistics. This view collects  and  ana-
	      lyzes  instruction  issue	 events.  This view is disabled	by de-
	      fault.

       -retire-stats
	      Enable extra retire control unit statistics. This	view  is  dis-
	      abled by default.

       -instruction-info
	      Enable the instruction info view.	This is	enabled	by default.

       -all-stats
	      Print all	hardware statistics. This enables extra	statistics re-
	      lated to the dispatch logic, the hardware	schedulers, the	regis-
	      ter  file(s),  and  the retire control unit. This	option is dis-
	      abled by default.

       -all-views
	      Enable all the view.

       -instruction-tables
	      Prints resource pressure information based on the	static	infor-
	      mation available from the	processor model. This differs from the
	      resource pressure	view because it	doesn't	require	that the  code
	      is  simulated. It	instead	prints the theoretical uniform distri-
	      bution of	resource pressure for every instruction	in sequence.

EXIT STATUS
       llvm-mca	returns	0 on success. Otherwise, an error message  is  printed
       to standard error, and the tool returns 1.

USING MARKERS TO ANALYZE SPECIFIC CODE BLOCKS
       llvm-mca	allows for the optional	usage of special code comments to mark
       regions of the assembly code to be analyzed.  A comment	starting  with
       substring  LLVM-MCA-BEGIN  marks	the beginning of a code	region.	A com-
       ment starting with substring LLVM-MCA-END marks the end of a  code  re-
       gion.  For example:

	  # LLVM-MCA-BEGIN My Code Region
	    ...
	  # LLVM-MCA-END

       Multiple	regions	can be specified provided that they do not overlap.  A
       code region can have an optional	description. If	no user-defined	region
       is specified, then llvm-mca assumes a default region which contains ev-
       ery instruction in the input file.  Every region	is analyzed in	isola-
       tion,  and the final performance	report is the union of all the reports
       generated for every code	region.

       Inline assembly directives may be used from source code to annotate the
       assembly	text:

	  int foo(int a, int b)	{
	    __asm volatile("# LLVM-MCA-BEGIN foo");
	    a += 42;
	    __asm volatile("# LLVM-MCA-END");
	    a *= b;
	    return a;
	  }

HOW LLVM-MCA WORKS
       llvm-mca	takes assembly code as input. The assembly code	is parsed into
       a sequence of MCInst with the help of the existing LLVM target assembly
       parsers.	 The  parsed sequence of MCInst	is then	analyzed by a Pipeline
       module to generate a performance	report.

       The Pipeline module simulates the execution of  the  machine  code  se-
       quence  in  a loop of iterations	(default is 100). During this process,
       the pipeline collects a number of execution related statistics. At  the
       end  of	this  process, the pipeline generates and prints a report from
       the collected statistics.

       Here is an example of a performance report generated by the tool	for  a
       dot-product  of two packed float	vectors	of four	elements. The analysis
       is conducted for	target x86, cpu	btver2.	 The following result  can  be
       produced	 via  the  following  command  using  the  example  located at
       test/tools/llvm-mca/X86/BtVer2/dot-product.s:

	  $ llvm-mca -mtriple=x86_64-unknown-unknown -mcpu=btver2 -iterations=300 dot-product.s

	  Iterations:	     300
	  Instructions:	     900
	  Total	Cycles:	     610
	  Total	uOps:	     900

	  Dispatch Width:    2
	  uOps Per Cycle:    1.48
	  IPC:		     1.48
	  Block	RThroughput: 2.0

	  Instruction Info:
	  [1]: #uOps
	  [2]: Latency
	  [3]: RThroughput
	  [4]: MayLoad
	  [5]: MayStore
	  [6]: HasSideEffects (U)

	  [1]	 [2]	[3]    [4]    [5]    [6]    Instructions:
	   1	  2	1.00			    vmulps	%xmm0, %xmm1, %xmm2
	   1	  3	1.00			    vhaddps	%xmm2, %xmm2, %xmm3
	   1	  3	1.00			    vhaddps	%xmm3, %xmm3, %xmm4

	  Resources:
	  [0]	- JALU0
	  [1]	- JALU1
	  [2]	- JDiv
	  [3]	- JFPA
	  [4]	- JFPM
	  [5]	- JFPU0
	  [6]	- JFPU1
	  [7]	- JLAGU
	  [8]	- JMul
	  [9]	- JSAGU
	  [10]	- JSTC
	  [11]	- JVALU0
	  [12]	- JVALU1
	  [13]	- JVIMUL

	  Resource pressure per	iteration:
	  [0]	 [1]	[2]    [3]    [4]    [5]    [6]	   [7]	  [8]	 [9]	[10]   [11]   [12]   [13]
	   -	  -	 -     2.00   1.00   2.00   1.00    -	   -	  -	 -	-      -      -

	  Resource pressure by instruction:
	  [0]	 [1]	[2]    [3]    [4]    [5]    [6]	   [7]	  [8]	 [9]	[10]   [11]   [12]   [13]   Instructions:
	   -	  -	 -	-     1.00    -	    1.00    -	   -	  -	 -	-      -      -	    vmulps	%xmm0, %xmm1, %xmm2
	   -	  -	 -     1.00    -     1.00    -	    -	   -	  -	 -	-      -      -	    vhaddps	%xmm2, %xmm2, %xmm3
	   -	  -	 -     1.00    -     1.00    -	    -	   -	  -	 -	-      -      -	    vhaddps	%xmm3, %xmm3, %xmm4

       According to this report, the dot-product kernel	has been executed  300
       times,  for  a total of 900 simulated instructions. The total number of
       simulated micro opcodes (uOps) is also 900.

       The report is structured	in three main  sections.   The	first  section
       collects	a few performance numbers; the goal of this section is to give
       a very quick overview of	the performance	throughput. Important  perfor-
       mance indicators	are IPC, uOps Per Cycle, and  Block RThroughput	(Block
       Reciprocal Throughput).

       IPC is computed dividing	the total number of simulated instructions  by
       the  total number of cycles. In the absence of loop-carried data	depen-
       dencies,	the observed IPC tends to a theoretical	maximum	which  can  be
       computed	 by  dividing the number of instructions of a single iteration
       by the Block RThroughput.

       Field 'uOps Per Cycle' is computed dividing the total number  of	 simu-
       lated micro opcodes by the total	number of cycles. A delta between Dis-
       patch Width and this field is an	indicator of a performance  issue.  In
       the  absence  of	loop-carried data dependencies,	the observed 'uOps Per
       Cycle' should tend to a theoretical maximum  throughput	which  can  be
       computed	 by  dividing  the number of uOps of a single iteration	by the
       Block RThroughput.

       Field uOps Per Cycle is bounded from above by the dispatch width.  That
       is  because  the	 dispatch  width limits	the maximum size of a dispatch
       group. Both IPC and 'uOps Per Cycle' are	limited	by the amount of hard-
       ware  parallelism.  The	availability of	hardware resources affects the
       resource	pressure distribution, and it limits the  number  of  instruc-
       tions  that  can	 be executed in	parallel every cycle.  A delta between
       Dispatch	Width and the theoretical maximum uOps per Cycle (computed  by
       dividing	 the  number  of  uOps	of  a  single  iteration  by the Block
       RTrhoughput) is an indicator of a performance bottleneck	caused by  the
       lack  of	hardware resources.  In	general, the lower the Block RThrough-
       put, the	better.

       In this example,	uOps per iteration/Block RThroughput  is  1.50.	 Since
       there  are no loop-carried dependencies,	the observed uOps Per Cycle is
       expected	to approach 1.50 when the number of iterations tends to	infin-
       ity.  The  delta	between	the Dispatch Width (2.00), and the theoretical
       maximum uOp throughput (1.50) is	an indicator of	a performance  bottle-
       neck  caused  by	the lack of hardware resources,	and the	Resource pres-
       sure view can help to identify the problematic resource usage.

       The second section of the  report  shows	 the  latency  and  reciprocal
       throughput  of every instruction	in the sequence. That section also re-
       ports extra information related to the number of	micro opcodes, and op-
       code properties (i.e., 'MayLoad', 'MayStore', and 'HasSideEffects').

       The third section is the	Resource pressure view.	 This view reports the
       average number of resource cycles consumed every	iteration by  instruc-
       tions  for  every processor resource unit available on the target.  In-
       formation is structured in two tables. The first	table reports the num-
       ber of resource cycles spent on average every iteration.	The second ta-
       ble correlates the resource cycles to the machine  instruction  in  the
       sequence. For example, every iteration of the instruction vmulps	always
       executes	on resource unit [6] (JFPU1 -  floating	 point	pipeline  #1),
       consuming  an  average of 1 resource cycle per iteration.  Note that on
       AMD Jaguar, vector floating-point multiply can only be issued to	 pipe-
       line  JFPU1,  while horizontal floating-point additions can only	be is-
       sued to pipeline	JFPU0.

       The resource pressure view helps	with identifying bottlenecks caused by
       high  usage  of	specific hardware resources.  Situations with resource
       pressure	mainly concentrated on a few resources should, in general,  be
       avoided.	  Ideally,  pressure  should  be uniformly distributed between
       multiple	resources.

   Timeline View
       The timeline view produces a  detailed  report  of  each	 instruction's
       state  transitions  through  an instruction pipeline.  This view	is en-
       abled by	the command line option	-timeline.  As instructions transition
       through	the  various stages of the pipeline, their states are depicted
       in the view report.  These states  are  represented  by	the  following
       characters:

       o D : Instruction dispatched.

       o e : Instruction executing.

       o E : Instruction executed.

       o R : Instruction retired.

       o = : Instruction already dispatched, waiting to	be executed.

       o - : Instruction executed, waiting to be retired.

       Below  is the timeline view for a subset	of the dot-product example lo-
       cated in	test/tools/llvm-mca/X86/BtVer2/dot-product.s and processed  by
       llvm-mca	using the following command:

	  $ llvm-mca -mtriple=x86_64-unknown-unknown -mcpu=btver2 -iterations=3	-timeline dot-product.s

	  Timeline view:
			      012345
	  Index	    0123456789

	  [0,0]	    DeeER.    .	   .   vmulps	%xmm0, %xmm1, %xmm2
	  [0,1]	    D==eeeER  .	   .   vhaddps	%xmm2, %xmm2, %xmm3
	  [0,2]	    .D====eeeER	   .   vhaddps	%xmm3, %xmm3, %xmm4
	  [1,0]	    .DeeE-----R	   .   vmulps	%xmm0, %xmm1, %xmm2
	  [1,1]	    . D=eeeE---R   .   vhaddps	%xmm2, %xmm2, %xmm3
	  [1,2]	    . D====eeeER   .   vhaddps	%xmm3, %xmm3, %xmm4
	  [2,0]	    .  DeeE-----R  .   vmulps	%xmm0, %xmm1, %xmm2
	  [2,1]	    .  D====eeeER  .   vhaddps	%xmm2, %xmm2, %xmm3
	  [2,2]	    .	D======eeeER   vhaddps	%xmm3, %xmm3, %xmm4

	  Average Wait times (based on the timeline view):
	  [0]: Executions
	  [1]: Average time spent waiting in a scheduler's queue
	  [2]: Average time spent waiting in a scheduler's queue while ready
	  [3]: Average time elapsed from WB until retire stage

		[0]    [1]    [2]    [3]
	  0.	 3     1.0    1.0    3.3       vmulps	%xmm0, %xmm1, %xmm2
	  1.	 3     3.3    0.7    1.0       vhaddps	%xmm2, %xmm2, %xmm3
	  2.	 3     5.7    0.0    0.0       vhaddps	%xmm3, %xmm3, %xmm4

       The  timeline  view  is	interesting because it shows instruction state
       changes during execution.  It also gives	an idea	of how the  tool  pro-
       cesses instructions executed on the target, and how their timing	infor-
       mation might be calculated.

       The timeline view is structured in two tables.  The first  table	 shows
       instructions  changing state over time (measured	in cycles); the	second
       table (named Average Wait  times)  reports  useful  timing  statistics,
       which  should help diagnose performance bottlenecks caused by long data
       dependencies and	sub-optimal usage of hardware resources.

       An instruction in the timeline view is identified by a pair of indices,
       where  the first	index identifies an iteration, and the second index is
       the instruction index (i.e., where it appears in	 the  code  sequence).
       Since this example was generated	using 3	iterations: -iterations=3, the
       iteration indices range from 0-2	inclusively.

       Excluding the first and last column, the	remaining columns are  in  cy-
       cles.  Cycles are numbered sequentially starting	from 0.

       From the	example	output above, we know the following:

       o Instruction [1,0] was dispatched at cycle 1.

       o Instruction [1,0] started executing at	cycle 2.

       o Instruction [1,0] reached the write back stage	at cycle 4.

       o Instruction [1,0] was retired at cycle	10.

       Instruction  [1,0]  (i.e.,  vmulps  from	iteration #1) does not have to
       wait in the scheduler's queue for the operands to become	available.  By
       the  time  vmulps  is  dispatched,  operands are	already	available, and
       pipeline	JFPU1 is ready to serve	another	instruction.  So the  instruc-
       tion  can  be  immediately issued on the	JFPU1 pipeline.	That is	demon-
       strated by the fact that	the instruction	only spent 1cy in  the	sched-
       uler's queue.

       There  is a gap of 5 cycles between the write-back stage	and the	retire
       event.  That is because instructions must retire	in program  order,  so
       [1,0]  has  to wait for [0,2] to	be retired first (i.e.,	it has to wait
       until cycle 10).

       In the example, all instructions	are in a RAW (Read After Write)	depen-
       dency  chain.   Register	%xmm2 written by vmulps	is immediately used by
       the first vhaddps, and register %xmm3 written by	the first  vhaddps  is
       used  by	 the second vhaddps.  Long data	dependencies negatively	impact
       the ILP (Instruction Level Parallelism).

       In the dot-product example, there are anti-dependencies	introduced  by
       instructions  from  different  iterations.  However, those dependencies
       can be removed at register renaming stage (at the  cost	of  allocating
       register	aliases, and therefore consuming physical registers).

       Table  Average  Wait  times  helps diagnose performance issues that are
       caused by the presence of long  latency	instructions  and  potentially
       long data dependencies which may	limit the ILP.	Note that llvm-mca, by
       default,	assumes	at least 1cy between the dispatch event	and the	 issue
       event.

       When  the  performance  is limited by data dependencies and/or long la-
       tency instructions, the number of cycles	spent while in the ready state
       is expected to be very small when compared with the total number	of cy-
       cles spent in the scheduler's queue.  The difference  between  the  two
       counters	 is  a good indicator of how large of an impact	data dependen-
       cies had	on the execution of the	 instructions.	 When  performance  is
       mostly limited by the lack of hardware resources, the delta between the
       two counters is small.  However,	the number  of	cycles	spent  in  the
       queue  tends to be larger (i.e.,	more than 1-3cy), especially when com-
       pared to	other low latency instructions.

   Extra Statistics to Further Diagnose	Performance Issues
       The -all-stats command line option enables extra	statistics and perfor-
       mance  counters	for the	dispatch logic,	the reorder buffer, the	retire
       control unit, and the register file.

       Below is	an example of -all-stats output	generated by  llvm-mca for 300
       iterations  of  the  dot-product	example	discussed in the previous sec-
       tions.

	  Dynamic Dispatch Stall Cycles:
	  RAT	  - Register unavailable:		       0
	  RCU	  - Retire tokens unavailable:		       0
	  SCHEDQ  - Scheduler full:			       272  (44.6%)
	  LQ	  - Load queue full:			       0
	  SQ	  - Store queue	full:			       0
	  GROUP	  - Static restrictions	on the dispatch	group: 0

	  Dispatch Logic - number of cycles where we saw N micro opcodes dispatched:
	  [# dispatched], [# cycles]
	   0,		   24  (3.9%)
	   1,		   272	(44.6%)
	   2,		   314	(51.5%)

	  Schedulers - number of cycles	where we saw N instructions issued:
	  [# issued], [# cycles]
	   0,	       7  (1.1%)
	   1,	       306  (50.2%)
	   2,	       297  (48.7%)

	  Scheduler's queue usage:
	  [1] Resource name.
	  [2] Average number of	used buffer entries.
	  [3] Maximum number of	used buffer entries.
	  [4] Total number of buffer entries.

	   [1]		  [2]	     [3]	[4]
	  JALU01	   0	      0		 20
	  JFPU01	   17	      18	 18
	  JLSAGU	   0	      0		 12

	  Retire Control Unit -	number of cycles where we saw N	instructions retired:
	  [# retired], [# cycles]
	   0,		109  (17.9%)
	   1,		102  (16.7%)
	   2,		399  (65.4%)

	  Total	ROB Entries:		    64
	  Max Used ROB Entries:		    35	( 54.7%	)
	  Average Used ROB Entries per cy:  32	( 50.0%	)

	  Register File	statistics:
	  Total	number of mappings created:    900
	  Max number of	mappings used:	       35

	  *  Register File #1 -- JFpuPRF:
	     Number of physical	registers:     72
	     Total number of mappings created: 900
	     Max number	of mappings used:      35

	  *  Register File #2 -- JIntegerPRF:
	     Number of physical	registers:     64
	     Total number of mappings created: 0
	     Max number	of mappings used:      0

       If we look at the Dynamic Dispatch  Stall  Cycles  table,  we  see  the
       counter for SCHEDQ reports 272 cycles.  This counter is incremented ev-
       ery time	the dispatch logic is unable to	dispatch a full	group  because
       the scheduler's queue is	full.

       Looking	at the Dispatch	Logic table, we	see that the pipeline was only
       able to dispatch	two micro opcodes 51.5%	of  the	 time.	 The  dispatch
       group was limited to one	micro opcode 44.6% of the cycles, which	corre-
       sponds to 272 cycles.  The dispatch statistics are displayed by	either
       using the command option	-all-stats or -dispatch-stats.

       The  next  table,  Schedulers, presents a histogram displaying a	count,
       representing the	number of instructions issued on some  number  of  cy-
       cles.   In  this	case, of the 610 simulated cycles, single instructions
       were issued 306 times (50.2%) and there were 7 cycles where no instruc-
       tions were issued.

       The  Scheduler's	 queue	usage table shows that the average and maximum
       number of buffer	entries	(i.e., scheduler queue entries)	used  at  run-
       time.   Resource	 JFPU01	 reached its maximum (18 of 18 queue entries).
       Note that AMD Jaguar implements three schedulers:

       o JALU01	- A scheduler for ALU instructions.

       o JFPU01	- A scheduler floating point operations.

       o JLSAGU	- A scheduler for address generation.

       The dot-product is a kernel of three  floating  point  instructions  (a
       vector  multiply	 followed  by two horizontal adds).  That explains why
       only the	floating point scheduler appears to be used.

       A full scheduler	queue is either	caused by data dependency chains or by
       a  sub-optimal  usage of	hardware resources.  Sometimes,	resource pres-
       sure can	be mitigated by	rewriting the kernel using different  instruc-
       tions  that  consume  different scheduler resources.  Schedulers	with a
       small queue are less resilient to bottlenecks caused by the presence of
       long  data dependencies.	 The scheduler statistics are displayed	by us-
       ing the command option -all-stats or -scheduler-stats.

       The next	table, Retire Control Unit, presents a histogram displaying  a
       count,  representing  the number	of instructions	retired	on some	number
       of cycles.  In this case, of the	610 simulated cycles, two instructions
       were retired during the same cycle 399 times (65.4%) and	there were 109
       cycles where no instructions were retired.  The retire  statistics  are
       displayed by using the command option -all-stats	or -retire-stats.

       The  last  table	 presented is Register File statistics.	 Each physical
       register	file (PRF) used	by the pipeline	is presented  in  this	table.
       In the case of AMD Jaguar, there	are two	register files,	one for	float-
       ing-point registers (JFpuPRF) and one  for  integer  registers  (JInte-
       gerPRF).	 The table shows that of the 900 instructions processed, there
       were 900	mappings created.  Since  this	dot-product  example  utilized
       only floating point registers, the JFPuPRF was responsible for creating
       the 900 mappings.  However, we see that the pipeline only used a	 maxi-
       mum of 35 of 72 available register slots	at any given time. We can con-
       clude that the floating point PRF was the only register file  used  for
       the  example, and that it was never resource constrained.  The register
       file statistics are displayed by	using the command option -all-stats or
       -register-file-stats.

       In this example,	we can conclude	that the IPC is	mostly limited by data
       dependencies, and not by	resource pressure.

   Instruction Flow
       This section describes the instruction flow through the	default	 pipe-
       line  of	 llvm-mca,  as	well  as  the functional units involved	in the
       process.

       The default pipeline implements the following sequence of  stages  used
       to process instructions.

       o Dispatch (Instruction is dispatched to	the schedulers).

       o Issue (Instruction is issued to the processor pipelines).

       o Write Back (Instruction is executed, and results are written back).

       o Retire	 (Instruction  is  retired; writes are architecturally commit-
	 ted).

       The default pipeline only models	the out-of-order portion of a  proces-
       sor.   Therefore,  the instruction fetch	and decode stages are not mod-
       eled. Performance  bottlenecks  in  the	frontend  are  not  diagnosed.
       llvm-mca	 assumes  that	instructions  have all been decoded and	placed
       into a queue before the simulation  start.   Also,  llvm-mca  does  not
       model branch prediction.

   Instruction Dispatch
       During  the  dispatch  stage,  instructions are picked in program order
       from a queue of already decoded instructions, and dispatched in	groups
       to the simulated	hardware schedulers.

       The  size  of a dispatch	group depends on the availability of the simu-
       lated hardware resources.  The processor	dispatch width defaults	to the
       value of	the IssueWidth in LLVM's scheduling model.

       An instruction can be dispatched	if:

       o The  size  of the dispatch group is smaller than processor's dispatch
	 width.

       o There are enough entries in the reorder buffer.

       o There are enough physical registers to	do register renaming.

       o The schedulers	are not	full.

       Scheduling models can  optionally  specify  which  register  files  are
       available  on the processor. llvm-mca uses that information to initial-
       ize register file descriptors.  Users can limit the number of  physical
       registers  that	are  globally available	for register renaming by using
       the command option -register-file-size.	A value	of zero	for  this  op-
       tion  means  unbounded. By knowing how many registers are available for
       renaming, the tool can predict dispatch stalls caused by	 the  lack  of
       physical	registers.

       The number of reorder buffer entries consumed by	an instruction depends
       on the number of	micro-opcodes specified	for that  instruction  by  the
       target  scheduling model.  The reorder buffer is	responsible for	track-
       ing the progress	of instructions	that  are  "in-flight",	 and  retiring
       them in program order.  The number of entries in	the reorder buffer de-
       faults to the value specified by	field MicroOpBufferSize	in the	target
       scheduling model.

       Instructions  that  are	dispatched to the schedulers consume scheduler
       buffer entries. llvm-mca	queries	the scheduling model to	determine  the
       set  of	buffered  resources  consumed by an instruction.  Buffered re-
       sources are treated like	scheduler resources.

   Instruction Issue
       Each processor scheduler	implements a buffer of instructions.   An  in-
       struction  has  to  wait	in the scheduler's buffer until	input register
       operands	become available.  Only	at that	point,	does  the  instruction
       becomes	 eligible   for	 execution  and	 may  be  issued  (potentially
       out-of-order) for execution.  Instruction  latencies  are  computed  by
       llvm-mca	with the help of the scheduling	model.

       llvm-mca's  scheduler is	designed to simulate multiple processor	sched-
       ulers.  The scheduler is	responsible for	 tracking  data	 dependencies,
       and dynamically selecting which processor resources are consumed	by in-
       structions.  It delegates the management	of  processor  resource	 units
       and resource groups to a	resource manager.  The resource	manager	is re-
       sponsible for selecting resource	units that are	consumed  by  instruc-
       tions.	For  example,  if  an  instruction  consumes 1cy of a resource
       group, the resource manager selects one of the available	units from the
       group;  by default, the resource	manager	uses a round-robin selector to
       guarantee that resource usage  is  uniformly  distributed  between  all
       units of	a group.

       llvm-mca's scheduler internally groups instructions into	three sets:

       o WaitSet: a set	of instructions	whose operands are not ready.

       o ReadySet: a set of instructions ready to execute.

       o IssuedSet: a set of instructions executing.

       Depending  on  the  operands  availability,  instructions that are dis-
       patched to the scheduler	are either placed into the WaitSet or into the
       ReadySet.

       Every cycle, the	scheduler checks if instructions can be	moved from the
       WaitSet to the ReadySet,	and if instructions from the ReadySet  can  be
       issued to the underlying	pipelines. The algorithm prioritizes older in-
       structions over younger instructions.

   Write-Back and Retire Stage
       Issued instructions are moved  from  the	 ReadySet  to  the  IssuedSet.
       There,  instructions  wait  until  they reach the write-back stage.  At
       that point, they	get removed from the queue and the retire control unit
       is notified.

       When  instructions  are executed, the retire control unit flags the in-
       struction as "ready to retire."

       Instructions are	retired	in program order.  The register	file is	 noti-
       fied  of	the retirement so that it can free the physical	registers that
       were allocated for the instruction during the register renaming stage.

   Load/Store Unit and Memory Consistency Model
       To simulate an out-of-order execution of	 memory	 operations,  llvm-mca
       utilizes	 a simulated load/store	unit (LSUnit) to simulate the specula-
       tive execution of loads and stores.

       Each load (or store) consumes an	entry in the load  (or	store)	queue.
       Users  can specify flags	-lqueue	and -squeue to limit the number	of en-
       tries in	the load and store queues respectively.	 The  queues  are  un-
       bounded by default.

       The  LSUnit implements a	relaxed	consistency model for memory loads and
       stores.	The rules are:

       1. A younger load is allowed to pass an older load only if there	are no
	  intervening stores or	barriers between the two loads.

       2. A  younger  load is allowed to pass an older store provided that the
	  load does not	alias with the store.

       3. A younger store is not allowed to pass an older store.

       4. A younger store is not allowed to pass an older load.

       By default, the LSUnit optimistically assumes that loads	do  not	 alias
       (-noalias=true) store operations.  Under	this assumption, younger loads
       are always allowed to pass older	stores.	 Essentially, the LSUnit  does
       not  attempt to run any alias analysis to predict when loads and	stores
       do not alias with each other.

       Note that, in the case of write-combining memory, rule 3	could  be  re-
       laxed to	allow reordering of non-aliasing store operations.  That being
       said, at	the moment, there is no	way to further relax the memory	 model
       (-noalias  is  the  only	 option).   Essentially, there is no option to
       specify a different memory  type	 (e.g.,	 write-back,  write-combining,
       write-through;  etc.)  and  consequently	 to weaken, or strengthen, the
       memory model.

       Other limitations are:

       o The LSUnit does not know when store-to-load forwarding	may occur.

       o The LSUnit does not know anything about cache	hierarchy  and	memory
	 types.

       o The  LSUnit  does not know how	to identify serializing	operations and
	 memory	fences.

       The LSUnit does not attempt to predict if  a  load  or  store  hits  or
       misses  the L1 cache.  It only knows if an instruction "MayLoad"	and/or
       "MayStore."  For	loads, the scheduling model provides  an  "optimistic"
       load-to-use  latency (which usually matches the load-to-use latency for
       when there is a hit in the L1D).

       llvm-mca	does not know about serializing	operations  or	memory-barrier
       like  instructions.  The	LSUnit conservatively assumes that an instruc-
       tion which has both "MayLoad" and unmodeled side	effects	behaves	like a
       "soft" load-barrier.  That means, it serializes loads without forcing a
       flush of	the load queue.	 Similarly, instructions that  "MayStore"  and
       have  unmodeled	side  effects are treated like store barriers.	A full
       memory barrier is a "MayLoad" and "MayStore" instruction	with unmodeled
       side effects.  This is inaccurate, but it is the	best that we can do at
       the moment with the current information available in LLVM.

       A load/store barrier consumes one entry of  the	load/store  queue.   A
       load/store  barrier  enforces ordering of loads/stores.	A younger load
       cannot pass a load barrier.  Also, a younger store cannot pass a	 store
       barrier.	 A younger load	has to wait for	the memory/load	barrier	to ex-
       ecute.  A load/store barrier is "executed" when it becomes  the	oldest
       entry in	the load/store queue(s). That also means, by construction, all
       of the older loads/stores have been executed.

       In conclusion, the full set of load/store consistency rules are:

       1. A store may not pass a previous store.

       2. A store may not pass a previous load (regardless of -noalias).

       3. A store has to wait until an older store barrier is fully executed.

       4. A load may pass a previous load.

       5. A load may not pass a	previous store unless -noalias is set.

       6. A load has to	wait until an older load barrier is fully executed.

AUTHOR
       Maintained by the LLVM Team (https://llvm.org/).

COPYRIGHT
       2003-2020, LLVM Project

8				  2020-08-27			   LLVM-MCA(1)

NAME | SYNOPSIS | DESCRIPTION | OPTIONS | EXIT STATUS | USING MARKERS TO ANALYZE SPECIFIC CODE BLOCKS | HOW LLVM-MCA WORKS | AUTHOR | COPYRIGHT

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