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Qin Zhao via llvm-dev

2016-Apr-22 21:59 UTC

[llvm-dev] RFC: EfficiencySanitizer Cache Fragmentation tool

Please reference the prior RFC on EfficiencySanitizer.  This is one of the
performance analysis tools we would like to build under the
EfficiencySanitizer umbrella.

===================
Motivation

===================
An application is running sub-optimally if only part of the data brought
into the cache is used, which we call cache fragmentation.  Knowing the
cache fragmentation information during a given application's execution
helps developers to understand the application’s cache behavior and how
best to direct performance optimization efforts. For example, developers
may reorder the struct fields based on the cache fragmentation information
and hopefully improve cache hit ration and performance.

===================
Approach

===================
We focus on two ways to get cache fragmentation information:

   1.

   Struct field access patterns.
   2.

   Heap/global object access patterns.


====================
Struct field access patterns

====================

   1.

   Get all the struct type information (e.g., via
   getIdentifiedStructTypes()), and create a counter for each field of each
   struct.
   2.

   Instrument each GEP (GetElementPtr) instruction if it operates on a
   struct type to update the corresponding field counter.
   3.

   At the program exit, filter and sort the struct field reference
   counters, and print the struct field hotness information for those structs
   deemed most likely to affect performance.  The sorting/filtering metric
   could include disparity between fields: hot fields interleaved with cold
   fields, with a total access count high enough to matter.


There are a few potential problems with this simple approach:

   -

   Overcount: a GEP instruction does not necessarily mean a field access.
   -

   Undercount: a GEP instruction may lead to multiple field accesses,
   especially if the address is passed to another function as an argument.
   -

   Racy update by multiple threads.

We want to keep the instrumentation simple in our initial implementation
for both robustness and performance reasons, so we will defer any analysis
(e.g., data flow analysis) to later stages. Any suggestions on how to
improve the accuracy are welcome.

There is one simple improvement we may want to explore: the temporal
locality of struct field accesses.

Two struct fields being hot (i.e., frequently accessed) does not
necessarily mean they are accessed together. We want to know the affinity
among those struct fields, which could be determined via a sampling
approach: track which fields are accessed together during the last period
at each sample, and update an affinity table for the final report.

We expect the time overhead of the tool to be well under the 5x
EfficiencySanitizer ceiling; presumably it should be under 2x.

==========================
Heap/global object access patterns

==========================
We plan to use shadow memory and sampling to keep track of heap/global
object accesses.

Shadow memory:
We use a 4byte-to-1byte shadow mapping.  Each application word is mapped to
a

   -

   shadow byte, and so a 64-byte cache line is mapped to a 16-byte shadow
   memory. In each shadow byte, the highest bit is used for indicating whether
   the corresponding application word is accessed, and the other 7 bits are
   used as a counter for the hotness of the application word.

   -

   Instrumentation:
   On every memory reference, the instrumented code simply checks if the
   highest bit is set. If not, the code sets it using an OR operation. We will
   live with races in updating shadow memory bits.
   -

   Sampling:
   On each sample we scan the shadow memory. If the highest bit of a shadow
   byte is set, we increment the 7-bit counter (to the maximum of 127; if this
   is found to be too small we could use separate storage for an additional
   counter for hot fields).
   -

   Memory allocation wrapping:
   When a heap object is freed, we acquire the callstack and its access
   pattern in the shadow memory. We may coalesce them based on the
   allocation/free callstack.


The report from the tool to the user at the end of execution would
essentially be a list of objects that have some significant fragmented
access pattern. We expect the time overhead of the tool to be well under
the 5x EfficiencySanitizer ceiling; presumably it should be under 3x.

We plan to implement both struct field access tracking and shadow based
heap/global object access tracking. In our initial implementation, we plan
to provide both results to developers simultaneously.

There are a number of alternative approaches that could be explored,
including a 4byte:4byte 32-bit hotness counter per 4-byte field, or a
1byte:1bit bitmap for field byte granularity with sampling.  Extensions to
the proposals above could also be explored in the future, such as combining
the struct and shadow modes for better results. Additionally, we may use
the 7 shadow bits differently to track temporal locality information
instead. Any suggestions are also welcome.


-- Qin
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Hal Finkel via llvm-dev

2016-Apr-23 00:13 UTC

head link

[llvm-dev] RFC: EfficiencySanitizer Cache Fragmentation tool

----- Original Message -----
> From: "Qin Zhao via llvm-dev" <llvm-dev at lists.llvm.org>
> To: "llvm-dev" <llvm-dev at lists.llvm.org>
> Sent: Friday, April 22, 2016 4:59:00 PM
> Subject: [llvm-dev] RFC: EfficiencySanitizer Cache Fragmentation tool
> Please reference the prior RFC on EfficiencySanitizer. This is one of
> the performance analysis tools we would like to build under the
> EfficiencySanitizer umbrella.
> ===================> Motivation
> ===================
> An application is running sub-optimally if only part of the data
> brought into the cache is used, which we call cache fragmentation.
> Knowing the cache fragmentation information during a given
> application's execution helps developers to understand the
> application’s cache behavior and how best to direct performance
> optimization efforts. For example, developers may reorder the struct
> fields based on the cache fragmentation information and hopefully
> improve cache hit ration and performance.
> ===================> Approach
> ===================
> We focus on two ways to get cache fragmentation information:
> 1.
> Struct field access patterns.
> 2.
> Heap/global object access patterns.
> ====================> Struct field access patterns
> ====================
> 1.
> Get all the struct type information (e.g., via
> getIdentifiedStructTypes()), and create a counter for each field of
> each struct.
> 2.
> Instrument each GEP (GetElementPtr) instruction if it operates on a
> struct type to update the corresponding field counter.
> 3.
> At the program exit, filter and sort the struct field reference
> counters, and print the struct field hotness information for those
> structs deemed most likely to affect performance. The
> sorting/filtering metric could include disparity between fields: hot
> fields interleaved with cold fields, with a total access count high
> enough to matter.
> There are a few potential problems with this simple approach:
> *
> Overcount: a GEP instruction does not necessarily mean a field
> access.
> *
> Undercount: a GEP instruction may lead to multiple field accesses,
> especially if the address is passed to another function as an
> argument.
I can't tell from your description, but it sounds like you might also
undercount accesses from escaped addresses (because these are later
indistinguishable from heap accesses)?
> *
> *
> Racy update by multiple threads.
> We want to keep the instrumentation simple in our initial
> implementation for both robustness and performance reasons, so we
> will defer any analysis (e.g., data flow analysis) to later stages.
> Any suggestions on how to improve the accuracy are welcome.I don't understand why you're using a separate mechanism here from what
is being used for heap accesses. Why don't you use the same shadow-memory
scheme here as you do for heap accesses (especially considering that escaped
stack addresses will be counted this way anyway), and then upon function exit,
collect the counts from the local stack? I think the necessary region is:

[@llvm.frameaddress(0), @llvm.frameaddress(0) + @llvm.get.dynamic.area.offset())

or you can call @llvm.stacksave() upon entry and use that as the base offset. 

-Hal 
> There is one simple improvement we may want to explore: the temporal
> locality of struct field accesses.
> Two struct fields being hot (i.e., frequently accessed) does not
> necessarily mean they are accessed together. We want to know the
> affinity among those struct fields, which could be determined via a
> sampling approach: track which fields are accessed together during
> the last period at each sample, and update an affinity table for the
> final report.
> We expect the time overhead of the tool to be well under the 5x
> EfficiencySanitizer ceiling; presumably it should be under 2x.
> ==========================> Heap/global object access patterns
> ==========================
> We plan to use shadow memory and sampling to keep track of
> heap/global object accesses.
> Shadow memory:
> We use a 4byte-to-1byte shadow mapping. Each application word is
> mapped to a
> *
> shadow byte, and so a 64-byte cache line is mapped to a 16-byte
> shadow memory. In each shadow byte, the highest bit is used for
> indicating whether the corresponding application word is accessed,
> and the other 7 bits are used as a counter for the hotness of the
> application word.
> *
> Instrumentation: On every memory reference, the instrumented code
> simply checks if the highest bit is set. If not, the code sets it
> using an OR operation. We will live with races in updating shadow
> memory bits.
> *
> Sampling: On each sample we scan the shadow memory. If the highest
> bit of a shadow byte is set, we increment the 7-bit counter (to the
> maximum of 127; if this is found to be too small we could use
> separate storage for an additional counter for hot fields).
> *
> Memory allocation wrapping: When a heap object is freed, we acquire
> the callstack and its access pattern in the shadow memory. We may
> coalesce them based on the allocation/free callstack.
> The report from the tool to the user at the end of execution would
> essentially be a list of objects that have some significant
> fragmented access pattern. We expect the time overhead of the tool
> to be well under the 5x EfficiencySanitizer ceiling; presumably it
> should be under 3x.
> We plan to implement both struct field access tracking and shadow
> based heap/global object access tracking. In our initial
> implementation, we plan to provide both results to developers
> simultaneously.
> There are a number of alternative approaches that could be explored,
> including a 4byte:4byte 32-bit hotness counter per 4-byte field, or
> a 1byte:1bit bitmap for field byte granularity with sampling.
> Extensions to the proposals above could also be explored in the
> future, such as combining the struct and shadow modes for better
> results. Additionally, we may use the 7 shadow bits differently to
> track temporal locality information instead. Any suggestions are
> also welcome.
> -- Qin
> _______________________________________________
> LLVM Developers mailing list
> llvm-dev at lists.llvm.org
> http://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
-- 

Hal Finkel 
Assistant Computational Scientist 
Leadership Computing Facility 
Argonne National Laboratory 
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Hal Finkel via llvm-dev

2016-Apr-23 00:18 UTC

head link

[llvm-dev] RFC: EfficiencySanitizer Cache Fragmentation tool

----- Original Message -----
> From: "Hal Finkel via llvm-dev" <llvm-dev at
lists.llvm.org>
> To: "Qin Zhao" <zhaoqin at google.com>
> Cc: "llvm-dev" <llvm-dev at lists.llvm.org>
> Sent: Friday, April 22, 2016 7:13:38 PM
> Subject: Re: [llvm-dev] RFC: EfficiencySanitizer Cache Fragmentation
> tool
> ----- Original Message -----
> > From: "Qin Zhao via llvm-dev" <llvm-dev at
lists.llvm.org>
> 
> > To: "llvm-dev" <llvm-dev at lists.llvm.org>
> 
> > Sent: Friday, April 22, 2016 4:59:00 PM
> 
> > Subject: [llvm-dev] RFC: EfficiencySanitizer Cache Fragmentation
> > tool
> 
> > Please reference the prior RFC on EfficiencySanitizer. This is one
> > of
> > the performance analysis tools we would like to build under the
> > EfficiencySanitizer umbrella.
> 
> > ===================> 
> > Motivation
> 
> > ===================> 
> > An application is running sub-optimally if only part of the data
> > brought into the cache is used, which we call cache fragmentation.
> > Knowing the cache fragmentation information during a given
> > application's execution helps developers to understand the
> > application’s cache behavior and how best to direct performance
> > optimization efforts. For example, developers may reorder the
> > struct
> > fields based on the cache fragmentation information and hopefully
> > improve cache hit ration and performance.
> 
> > ===================> 
> > Approach
> 
> > ===================> 
> > We focus on two ways to get cache fragmentation information:
> 
> > 1.
> 
> > Struct field access patterns.
> 
> > 2.
> 
> > Heap/global object access patterns.
> 
> > ====================> 
> > Struct field access patterns
> 
> > ====================> 
> > 1.
> 
> > Get all the struct type information (e.g., via
> > getIdentifiedStructTypes()), and create a counter for each field of
> > each struct.
> 
> > 2.
> 
> > Instrument each GEP (GetElementPtr) instruction if it operates on a
> > struct type to update the corresponding field counter.
> 
> > 3.
> 
> > At the program exit, filter and sort the struct field reference
> > counters, and print the struct field hotness information for those
> > structs deemed most likely to affect performance. The
> > sorting/filtering metric could include disparity between fields:
> > hot
> > fields interleaved with cold fields, with a total access count high
> > enough to matter.
> 
> > There are a few potential problems with this simple approach:
> 
> > *
> 
> > Overcount: a GEP instruction does not necessarily mean a field
> > access.
> 
> > *
> 
> > Undercount: a GEP instruction may lead to multiple field accesses,
> > especially if the address is passed to another function as an
> > argument.
> 
You can ignore my message; I misread the distinction here. 

-Hal 
> > *
> 
> I can't tell from your description, but it sounds like you might also
> undercount accesses from escaped addresses (because these are later
> indistinguishable from heap accesses)?
> > *
> 
> > *
> 
> > Racy update by multiple threads.
> 
> > We want to keep the instrumentation simple in our initial
> > implementation for both robustness and performance reasons, so we
> > will defer any analysis (e.g., data flow analysis) to later stages.
> > Any suggestions on how to improve the accuracy are welcome.
> 
> I don't understand why you're using a separate mechanism here from
> what is being used for heap accesses. Why don't you use the same
> shadow-memory scheme here as you do for heap accesses (especially
> considering that escaped stack addresses will be counted this way
> anyway), and then upon function exit, collect the counts from the
> local stack? I think the necessary region is:
> [@llvm.frameaddress(0), @llvm.frameaddress(0) +
> @llvm.get.dynamic.area.offset())
> or you can call @llvm.stacksave() upon entry and use that as the base
> offset.
> -Hal
> > There is one simple improvement we may want to explore: the
> > temporal
> > locality of struct field accesses.
> 
> > Two struct fields being hot (i.e., frequently accessed) does not
> > necessarily mean they are accessed together. We want to know the
> > affinity among those struct fields, which could be determined via a
> > sampling approach: track which fields are accessed together during
> > the last period at each sample, and update an affinity table for
> > the
> > final report.
> 
> > We expect the time overhead of the tool to be well under the 5x
> > EfficiencySanitizer ceiling; presumably it should be under 2x.
> 
> > ==========================> 
> > Heap/global object access patterns
> 
> > ==========================> 
> > We plan to use shadow memory and sampling to keep track of
> > heap/global object accesses.
> 
> > Shadow memory:
> 
> > We use a 4byte-to-1byte shadow mapping. Each application word is
> > mapped to a
> 
> > *
> 
> > shadow byte, and so a 64-byte cache line is mapped to a 16-byte
> > shadow memory. In each shadow byte, the highest bit is used for
> > indicating whether the corresponding application word is accessed,
> > and the other 7 bits are used as a counter for the hotness of the
> > application word.
> 
> > *
> 
> > Instrumentation: On every memory reference, the instrumented code
> > simply checks if the highest bit is set. If not, the code sets it
> > using an OR operation. We will live with races in updating shadow
> > memory bits.
> 
> > *
> 
> > Sampling: On each sample we scan the shadow memory. If the highest
> > bit of a shadow byte is set, we increment the 7-bit counter (to the
> > maximum of 127; if this is found to be too small we could use
> > separate storage for an additional counter for hot fields).
> 
> > *
> 
> > Memory allocation wrapping: When a heap object is freed, we acquire
> > the callstack and its access pattern in the shadow memory. We may
> > coalesce them based on the allocation/free callstack.
> 
> > The report from the tool to the user at the end of execution would
> > essentially be a list of objects that have some significant
> > fragmented access pattern. We expect the time overhead of the tool
> > to be well under the 5x EfficiencySanitizer ceiling; presumably it
> > should be under 3x.
> 
> > We plan to implement both struct field access tracking and shadow
> > based heap/global object access tracking. In our initial
> > implementation, we plan to provide both results to developers
> > simultaneously.
> 
> > There are a number of alternative approaches that could be
> > explored,
> > including a 4byte:4byte 32-bit hotness counter per 4-byte field, or
> > a 1byte:1bit bitmap for field byte granularity with sampling.
> > Extensions to the proposals above could also be explored in the
> > future, such as combining the struct and shadow modes for better
> > results. Additionally, we may use the 7 shadow bits differently to
> > track temporal locality information instead. Any suggestions are
> > also welcome.
> 
> > -- Qin
> 
> > _______________________________________________
> 
> > LLVM Developers mailing list
> 
> > llvm-dev at lists.llvm.org
> 
> > http://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
> 
> --
> Hal Finkel
> Assistant Computational Scientist
> Leadership Computing Facility
> Argonne National Laboratory
> _______________________________________________
> LLVM Developers mailing list
> llvm-dev at lists.llvm.org
> http://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
-- 

Hal Finkel 
Assistant Computational Scientist 
Leadership Computing Facility 
Argonne National Laboratory 
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