source: doc/theses/mubeen_zulfiqar_MMath/benchmarks.tex @ f6e6a55

\chapter{Benchmarks}

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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Micro Benchmark Suite
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The aim of micro benchmark suite is to create a set of programs that can evaluate a memory allocator based on the
performance matrices described in (FIX ME: local cite). These programs can be taken as a standard to benchmark an
allocator's basic goals. These programs give details of an allocator's memory overhead and speed under a certain
allocation pattern. The speed of the allocator is benchmarked in different ways. Similarly, false sharing happening in
an allocator is also measured in multiple ways. These benchmarks evalute the allocator under a certain allocation
pattern which is configurable and can be changed using a few knobs to benchmark observe an allocator's performance
under a desired allocation pattern.

Micro Benchmark Suite benchmarks an allocator's performance by allocating dynamic objects and, then, measuring specifc
matrices. The benchmark suite evaluates an allocator with a certain allocation pattern. Bnechmarks have different knobs
that can be used to change allocation pattern and evaluate an allocator under desired conditions. These can be set by
giving commandline arguments to the benchmark on execution.

\section{Current Benchmarks} There are multiple benchmarks that are built individually and evaluate different aspects of
 a memory allocator. But, there is not a set of benchamrks that can be used to evaluate multiple aspects of memory
 allocators.

\subsection{threadtest}(FIX ME: cite benchmark and hoard) Each thread repeatedly allocates and then deallocates 100,000
 objects. Runtime of the benchmark evaluates its efficiency.

\subsection{shbench}(FIX ME: cite benchmark and hoard) Each thread allocates and randomly frees a number of random-sized
 objects. It is a stress test that also uses runtime to determine efficiency of the allocator.

\subsection{larson}(FIX ME: cite benchmark and hoard) Larson simulates a server environment. Multiple threads are
 created where each thread allocator and free a number of objects within a size range. Some objects are passed from
 threads to the child threads to free. It caluculates memory operations per second as an indicator of memory
 allocator's performance.

\section{Memory Benchmark} Memory benchmark measures memory overhead of an allocator. It allocates a number of dynamic
 objects. Then, by reading /self/proc/maps, gets the total memory that the allocator has reuested from the OS. It
 calculates the memory head by taking the difference between the memory the allocator has requested from the OS and the
 memory that program has allocated.

\begin{figure}
\centering
\includegraphics[width=1\textwidth]{figures/bench-memory.eps}
\caption{Benchmark Memory Overhead}
\label{fig:benchMemoryFig}
\end{figure}

Figure \ref{fig:benchMemoryFig} gives a flow of the memory benchmark. It creates a producer-consumer scenerio with K producers
 and each producer has M consumers. Producer has a separate buffer for each consumer. Producer allocates N objects of
 random sizes following the given distrubution for each consumer. Consumer frees those objects. After every memory
 operation, program memory usage is recorded throughout the runtime. This data then can be used to visualize the memory
 usage and consumption of the prigram.

Different knobs can be adjusted to set certain thread model.\\
-threadA :  sets number of alloc threads (producers) for mem benchmark\\
-consumeS:  sets production and conumption round size\\
-threadF :  sets number of free threads (consumers) for mem benchmark

Object allocation size can be changed using the knobs:\\
-maxS    :  sets max object size\\
-minS    :  sets min object size\\
-stepS   :  sets object size increment\\
-distroS :  sets object size distribution\\
-objN    :  sets number of objects per thread\\

\section{Speed Benchmark} Speed benchmark measures the runtime speed of an allocator (FIX ME: cite allocator routines).
 Speed benchmark measures runtime speed of individual memory allocation routines. It also considers different
 allocation chains to measures the performance of the allocator by combining multiple allocation routines in a chain.
 It uses following chains and measures allocator runtime speed against them:
\begin{itemize}
\item malloc 0
\item free NULL
\item malloc
\item realloc
\item free
\item calloc
\item malloc-free
\item realloc-free
\item calloc-free
\item malloc-realloc
\item calloc-realloc
\item malloc-realloc-free
\item calloc-realloc-free
\item malloc-realloc-free-calloc
\end{itemize}

\begin{figure}
\centering
\includegraphics[width=1\textwidth]{figures/bench-speed.eps}
\caption{Benchmark Speed}
\label{fig:benchSpeedFig}
\end{figure}

As laid out in figure \ref{fig:benchSpeedFig}, each chain is measured separately. Each routine in the chain is called for N objects and then
 those allocated objects are used when call the next routine in the allocation chain. This way we can measure the
 complete latency of memory allocator when multiple routines are chained together e.g. malloc-realloc-free-calloc gives
 us the whole picture of the major allocation routines when combined together in a chain.

For each chain, time taken is recorded which then can be used to visualize performance of a memory allocator against
each chain.

Number of worker threads can be adjust using a command-line argument -threadN.

\section{Churn Benchmark} Churn benchmark measures the overall runtime speed of an allocator in a multi-threaded
 scenerio where each thread extinsevly allocates and frees dynamic memory.

\begin{figure}
\centering
\includegraphics[width=1\textwidth]{figures/bench-churn.eps}
\caption{Benchmark Churn}
\label{fig:benchChurnFig}
\end{figure}

Figure \ref{fig:benchChurnFig} illustrates churn benchmark.
 This benchmark creates a buffer with M spots and starts K threads. Each thread randomly picks a
 spot out of M spots, it frees the object currently at that spot and allocates a new object for that spot. Each thread
 repeats this cycle for N times. Main threads measures the total time taken for the whole benchmark and that time is
 used to evaluate memory allocator's performance.

Only malloc and free are used to allocate and free an object to eliminate any extra cost such as memcpy in realloc etc.
Malloc/free allows us to measure latency of memory allocation only without paying any extra cost. Churn simulates a
memory intensive program that can be tuned to create different scenerios.

Following commandline arguments can be used to tune the benchmark.\\
-threadN :  sets number of threads, K\\
-cSpots  :  sets number of spots for churn, M\\
-objN    :  sets number of objects per thread, N\\
-maxS    :  sets max object size\\
-minS    :  sets min object size\\
-stepS   :  sets object size increment\\
-distroS :  sets object size distribution

\section{Cache Thrash}\label{sec:benchThrashSec} Cache Thrash benchmark measures allocator induced active false sharing
 in an allocator as illustrated in figure \ref{f:AllocatorInducedActiveFalseSharing}.
 If memory allocator allocates memory for multiple threads on
 same cache line, this can slow down the program performance. If both threads, who share one cache line, frequently
 read/write to their object on the cache line concurrently then this will cause cache miss everytime a thread accesse
 the object as the other thread might have written something at their memory location on the same cache line.

\begin{figure}
\centering
\includegraphics[width=1\textwidth]{figures/bench-cache-thrash.eps}
\caption{Benchmark Allocator Induced Active False Sharing}
\label{fig:benchThrashFig}
\end{figure}

Cache thrash tries to create a scenerio that should lead to allocator induced false sharing if the underlying memory
allocator is allocating dynamic memory to multiple threads on same cache lines. Ideally, a memory allocator should
distance dynamic memory region of one thread from other threads'. Having multiple threads allocating small objects
simultanously should cause the memory allocator to allocate objects for multiple objects on the same cache line if its
not distancing the memory among different threads.

Figure \ref{fig:benchThrashFig} lays out flow of the cache thrash benchmark.
 It creates K worker threads. Each worker thread allocates an object and intensively read/write
 it for M times to invalidate cache lines frequently to slow down other threads who might be sharing this cache line
 with it. Each thread repeats this for N times. Main thread measures the total time taken to for all worker threads to
 complete. Worker threads sharing cahche lines with each other will take longer.

Different cache access scenerios can be created using the following commandline arguments.\\
-threadN :  sets number of threads, K\\
-cacheIt :  iterations for cache benchmark, N\\
-cacheRep:  repetations for cache benchmark, M\\
-cacheObj:  object size for cache benchmark

\section{Cache Scratch} Cache Scratch benchmark measures allocator induced passive false sharing in an allocator. An
 allocator can unintentionally induce false sharing depending upon its management of the freed objects as described in
 figure \ref{f:AllocatorInducedPassiveFalseSharing}. If a thread A allocates multiple objects together then they will be
  possibly allocated on the same cache line by the memory allocator. If the thread now passes this object to another
  thread B then the two of them will sharing the same cache line but this scenerio is not induced by the allocator.
  Instead, the program induced this situation. Now it might be possible that if thread B frees this object and then
  allocate an object of the same size then the allocator may return the same object which is on a cache line shared
  with thread A. Now this false sharing is being caused by the memory allocator although it was started by the
  program.

\begin{figure}
\centering
\includegraphics[width=1\textwidth]{figures/bench-cache-scratch.eps}
\caption{Benchmark Program Induced Passive False Sharing}
\label{fig:benchScratchFig}
\end{figure}

Cache scratch main thread induces false sharing and creates a scenerio that should make memory allocator preserve the
 program-induced false sharing if it does not retur a freed object to its owner thread and, instead, re-uses it
 instantly. An alloator using object ownership, as described in section \ref{s:Ownership}, would be less susceptible to allocator induced passive
 false sharing. If the object is returned to the thread who owns it or originally allocated it then the thread B will
 get a new object that will be less likely to be on the same cache line as thread A.

As in figure \ref{fig:benchScratchFig}, cache Scratch allocates K dynamic objects together, one for each of the K worker threads,
 possibly causing memory allocator to allocate these objects on the same cache-line. Then it create K worker threads and passes
 an object from the K allocated objects to each of the K threads. Each worker thread frees the object passed by the main thread.
 Then, it allocates an object and reads/writes it repetitively for M times causing frequent cache invalidations. Each worker
 repeats this for N times.

Each thread allocating an object after freeing the original object passed by the main thread should cause the memory
allocator to return the same object that was initially allocated by the main thread if the allocator did not return the
intial object bakc to its owner (main thread). Then, intensive read/write on the shared cache line by multiple threads
should slow down worker threads due to to high cache invalidations and misses. Main thread measures the total time
taken for all the workers to complete.

Similar to bechmark cache thrash in section \ref{sec:benchThrashSec}, different cache access scenerios can be created using the following commandline arguments.\\
-threadN :  sets number of threads, K\\
-cacheIt :  iterations for cache benchmark, N\\
-cacheRep:  repetations for cache benchmark, M\\
-cacheObj:  object size for cache benchmark