[llvm-dev] RFC: EfficiencySanitizer

Tue Apr 19 23:14:12 PDT 2016

Interesting idea!  I understand how the bookkeeping in the tool is similar to some of the sanitizers but I am wondering whether that is really the best developer’s work-flow for such a tool.

I could imagine that some of the opportunities discovered by the tool could be optimized automatically by the compiler (e.g. temporal loads, sw prefetching, partitioning the heap) so feeding this information back to the compiler could be highly useful.  I am wondering whether the PGO model is closer to what we want at the end.  The problem can also be thought of as a natural extension of PGO.  Besides instrumenting branches and indirect calls, it adds instrumentation for loads and stores.

We have internally been discussing ways to use PGO for optimization diagnostics (a continuation of Tyler’s work, see http://blog.llvm.org/2014/11/loop-vectorization-diagnostics-and.html <http://blog.llvm.org/2014/11/loop-vectorization-diagnostics-and.html>).  The idea is to help the developer to focus in on opportunities in hot code. It seems that the diagnostics provided by your tools could be emitted directly by the analyses in LLVM during the profile-use phase.

Adam

> On Apr 17, 2016, at 2:46 PM, Derek Bruening via llvm-dev <llvm-dev at lists.llvm.org> wrote:
> 
> TL;DR: We plan to build a suite of compiler-based dynamic instrumentation tools for analyzing targeted performance problems.  These tools will all live under a new "EfficiencySanitizer" (or "esan") sanitizer umbrella, as they will share significant portions of their implementations.
> 
> ====================
> Motivation
> ====================
> 
> Our goal is to build a suite of dynamic instrumentation tools for analyzing particular performance problems that are difficult to evaluate using other profiling methods.  Modern hardware performance counters provide insight into where time is spent and when micro-architectural events such as cache misses are occurring, but they are of limited effectiveness for contextual analysis: it is not easy to answer *why* a cache miss occurred.
> 
> Examples of tools that we have planned include: identifying wasted or redundant computation, identifying cache fragmentation, and measuring working sets.  See more details on these below.
> 
> ====================
> Approach
> ====================
> 
> We believe that tools with overhead beyond about 5x are simply too heavyweight to easily apply to large, industrial-sized applications running real-world workloads.  Our goal is for our tools to gather useful information with overhead less than 5x, and ideally closer to 3x, to facilitate deployment.  We would prefer to trade off accuracy and build a less-accurate tool below our overhead ceiling than to build a high-accuracy but slow tool.  We hope to hit a sweet spot of tools that gather trace-based contextual information not feasible with pure sampling yet are still practical to deploy.
> 
> In a similar vein, we would prefer a targeted tool that analyzes one particular aspect of performance with low overhead than a more general tool that can answer more questions but has high overhead.
> 
> Dynamic binary instrumentation is one option for these types of tools, but typically compiler-based instrumentation provides better performance, and we intend to focus only on analyzing applications for which source code is available.  Studying instruction cache behavior with compiler instrumentation can be challenging, however, so we plan to at least initially focus on data performance.
> 
> Many of our planned tools target specific performance issues with data accesses.  They employ the technique of *shadow memory* to store metadata about application data references, using the compiler to instrument loads and stores with code to update the shadow memory.  A companion runtime library intercepts libc calls if necessary to update shadow memory on non-application data references.  The runtime library also intercepts heap allocations and other key events in order to perform its analyses.  This is all very similar to how existing sanitizers such as AddressSanitizer, ThreadSanitizer, MemorySanitizer, etc. operate today.
> 
> ====================
> Example Tools
> ====================
> 
> We have several initial tools that we plan to build.  These are not necessarily novel ideas on their own: some of these have already been explored in academia.  The idea is to create practical, low-overhead, robust, and publicly available versions of these tools.
> 
> *Cache fragmentation*: this tool gather data structure field hotness information, looking for data layout optimization opportunities by grouping hot fields together to avoid data cache fragmentation.  Future enhancements may add field affinity information if it can be computed with low enough overhead.
> 
> *Working set measurement*: this tool measures the data working set size of an application at each snapshot during execution.  It can help to understand phased behavior as well as providing basic direction for further effort by the developer: e.g., knowing whether the working set is close to fitting in current L3 caches or is many times larger can help determine where to spend effort.
> 
> *Dead store detection*: this tool identifies dead stores (write-after-write patterns with no intervening read) as well as redundant stores (writes of the same value already in memory).  Xref the Deadspy paper from CGO 2012.
> 
> *Single-reference*: this tool identifies data cache lines brought in but only read once.  These could be candidates for non-temporal loads.
> 
> ====================
> EfficiencySanitizer
> ====================
> 
> We are proposing the name EfficiencySanitizer, or "esan" for short, to refer to this suite of dynamic instrumentation tools for improving program efficiency.  As we have a number of different tools that share quite a bit of their implementation we plan to consider them sub-tools under the EfficiencySanitizer umbrella, rather than adding a whole bunch of separate instrumentation and runtime library components.
> 
> While these tools are not addressing correctness issues like other sanitizers, they will be sharing a lot of the existing sanitizer runtime library support code.  Furthermore, users are already familiar with the sanitizer brand, and it seems better to extend that concept rather than add some new term.
> 
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