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  • doc/theses/mubeen_zulfiqar_MMath/.gitignore

    rdcbfcbc r07b4970  
    11# Intermediate Results:
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    44# Final Files:

  • doc/theses/mubeen_zulfiqar_MMath/benchmarks.tex

    rdcbfcbc r07b4970  
    4141%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    4242
    43 \section{Performance Matrices of Memory Allocators}
     43\section Performance Matrices of Memory Allocators
    4444
    4545When it comes to memory allocators, there are no set standards of performance. Performance of a memory allocator depends highly on the usage pattern of the application. A memory allocator that is the best performer for a certain application X might be the worst for some other application which has completely different memory usage pattern compared to the application X. It is extremely difficult to make one universally best memory allocator which will outperform every other memory allocator for every usage pattern. So, there is a lack of a set of standard benchmarks that are used to evaluate a memory allocators's performance.
    4646
    4747If we breakdown the goals of a memory allocator, there are two basic matrices on which a memory allocator's performance is evaluated.
    48 \begin{enumerate}
    49 \item
    50 Memory Overhead
    51 \item
    52 Speed
    53 \end{enumerate}
    54 
    55 \subsection{Memory Overhead}
    56 Memory overhead is the extra memory that a memory allocator takes from OS which is not requested by the application. Ideally, an allocator should get just enough memory from OS that can fulfill application's request and should return this memory to OS as soon as applications frees it. But, allocators retain more memory compared to what application has asked for which causes memory overhead. Memory overhead can happen for various reasons.
    57 
    58 \subsubsection{Fragmentation}
    59 Fragmentation is one of the major reasons behind memory overhead. Fragmentation happens because of situations that are either necassary for proper functioning of the allocator such as internal memory management and book-keeping or are out of allocator's control such as application's usage pattern.
    60 
    61 \paragraph{Internal Fragmentation}
    62 For internal book-keeping, allocators divide raw memory given by OS into chunks, blocks, or lists that can fulfill application's requested size. Allocators use memory given by OS for creating headers, footers etc. to store information about these chunks, blocks, or lists. This increases usage of memory in-addition to the memory requested by application as the allocators need to store their book-keeping information. This extra usage of memory for allocator's own book-keeping is called Internal Fragmentation. Although it cases memory overhead but this overhead is necassary for an allocator's proper funtioning.
     48
     491. Memory Overhead
     502. Speed
     51
     52        /subsection Memory Overhead
     53        Memory overhead is the extra memory that a memory allocator takes from OS which is not requested by the application. Ideally, an allocator should get just enough memory from OS that can fulfill application's request and should return this memory to OS as soon as applications frees it. But, allocators retain more memory compared to what application has asked for which causes memory overhead. Memory overhead can happen for various reasons.
     54
     55                /subsubsection Fragmentation
     56                Fragmentation is one of the major reasons behind memory overhead. Fragmentation happens because of situations that are either necassary for proper functioning of the allocator such as internal memory management and book-keeping or are out of allocator's control such as application's usage pattern.
     57
     58                        /subsubsubsection Internal Fragmentation
     59                        For internal book-keeping, allocators divide raw memory given by OS into chunks, blocks, or lists that can fulfill application's requested size. Allocators use memory given by OS for creating headers, footers etc. to store information about these chunks, blocks, or lists. This increases usage of memory in-addition to the memory requested by application as the allocators need to store their book-keeping information. This extra usage of memory for allocator's own book-keeping is called Internal Fragmentation. Although it cases memory overhead but this overhead is necassary for an allocator's proper funtioning.
     60
    6361
    6462*** FIX ME: Insert a figure of internal fragmentation with explanation
    6563
    66 \paragraph{External Fragmentation}
    67 External fragmentation is the free bits of memory between or around chunks of memory that are currently in-use of the application. Segmentation in memory due to application's usage pattern causes external fragmentation. The memory which is part of external fragmentation is completely free as it is neither used by allocator's internal book-keeping nor by the application. Ideally, an allocator should return a segment of memory back to the OS as soon as application frees it. But, this is not always the case. Allocators get memory from OS in one of the two ways.
    68 
    69 \begin{itemize}
    70 \item
    71 MMap: an allocator can ask OS for whole pages in mmap area. Then, the allocator segments the page internally and fulfills application's request.
    72 \item
    73 Heap: an allocator can ask OS for memory in heap area using system calls such as sbrk. Heap are grows downwards and shrinks upwards.
    74 \begin{itemize}
    75 \item
    76 If an allocator uses mmap area, it can only return extra memory back to OS if the whole page is free i.e. no chunk on the page is in-use of the application. Even if one chunk on the whole page is currently in-use of the application, the allocator has to retain the whole page.
    77 \item
    78 If an allocator uses the heap area, it can only return the continous free memory at the end of the heap area that is currently in allocator's possession as heap area shrinks upwards. If there are free bits of memory in-between chunks of memory that are currently in-use of the application, the allocator can not return these free bits.
    79 
    80 *** FIX ME: Insert a figure of above scenrio with explanation
    81 \item
    82 Even if the entire heap area is free except one small chunk at the end of heap area that is being used by the application, the allocator cannot return the free heap area back to the OS as it is not a continous region at the end of heap area.
    83 
    84 *** FIX ME: Insert a figure of above scenrio with explanation
    85 
    86 \item
    87 Such scenerios cause external fragmentation but it is out of the allocator's control and depend on application's usage pattern.
    88 \end{itemize}
    89 \end{itemize}
    90 
    91 \subsubsection{Internal Memory Management}
    92 Allocators such as je-malloc (FIX ME: insert reference) pro-actively get some memory from the OS and divide it into chunks of certain sizes that can be used in-future to fulfill application's request. This causes memory overhead as these chunks are made before application's request. There is also the possibility that an application may not even request memory of these sizes during their whole life-time.
    93 
    94 *** FIX ME: Insert a figure of above scenrio with explanation
    95 
    96 Allocators such as rp-malloc (FIX ME: insert reference) maintain lists or blocks of sized memory segments that is freed by the application for future use. These lists are maintained without any guarantee that application will even request these sizes again.
    97 
    98 Such tactics are usually used to gain speed as allocator will not have to get raw memory from OS and manage it at the time of application's request but they do cause memory overhead.
    99 
    100 Fragmentation and managed sized chunks of free memory can lead to Heap Blowup as the allocator may not be able to use the fragments or sized free chunks of free memory to fulfill application's requests of other sizes.
    101 
    102 \subsection{Speed}
    103 When it comes to performance evaluation of any piece of software, its runtime is usually the first thing that is evaluated. The same is true for memory allocators but, in case of memory allocators, speed does not only mean the runtime of memory allocator's routines but there are other factors too.
    104 
    105 \subsubsection{Runtime Speed}
    106 Low runtime is the main goal of a memory allocator when it comes it proving its speed. Runtime is the time that it takes for a routine of memory allocator to complete its execution. As mentioned in (FIX ME: refernce to routines' list), there four basic routines that are used in memory allocation. Ideally, each routine of a memory allocator should be fast. Some memory allocator designs use pro-active measures (FIX ME: local refernce) to gain speed when allocating some memory to the application. Some memory allocators do memory allocation faster than memory freeing (FIX ME: graph refernce) while others show similar speed whether memory is allocated or freed.
    107 
    108 \subsubsection{Memory Access Speed}
    109 Runtime speed is not the only speed matrix in memory allocators. The memory that a memory allocator has allocated to the application also needs to be accessible as quick as possible. The application should be able to read/write allocated memory quickly. The allocation method of a memory allocator may introduce some delays when it comes to memory access speed, which is specially important in concurrent applications. Ideally, a memory allocator should allocate all memory on a cache-line to only one thread and no cache-line should be shared among multiple threads. If a memory allocator allocates memory to multple threads on a same cache line, then cache may get invalidated more frequesntly when two different threads running on two different processes will try to read/write the same memory region. On the other hand, if one cache-line is used by only one thread then the cache may get invalidated less frequently. This sharing of one cache-line among multiple threads is called false sharing (FIX ME: cite wasik).
    110 
    111 \paragraph{Active False Sharing}
    112 Active false sharing is the sharing of one cache-line among multiple threads that is caused by memory allocator. It happens when two threads request memory from memory allocator and the allocator allocates memory to both of them on the same cache-line. After that, if the threads are running on different processes who have their own caches and both threads start reading/writing the allocated memory simultanously, their caches will start getting invalidated every time the other thread writes something to the memory. This will cause the application to slow down as the process has to load cache much more frequently.
    113 
    114 *** FIX ME: Insert a figure of above scenrio with explanation
    115 
    116 \paragraph{Passive False Sharing}
    117 Passive false sharing is the kind of false sharing which is caused by the application and not the memory allocator. The memory allocator may preservce passive false sharing in future instead of eradicating it. But, passive false sharing is initiated by the application.
    118 
    119 \subparagraph{Program Induced Passive False Sharing}
    120 Program induced false sharing is completely out of memory allocator's control and is purely caused by the application. When a thread in the application creates multiple objects in the dynamic area and allocator allocates memory for these objects on the same cache-line as the objects are created by the same thread. Passive false sharing will occur if this thread passes one of these objects to another thread but it retains the rest of these objects or it passes some/all of the remaining objects to some third thread(s). Now, one cache-line is shared among multiple threads but it is caused by the application and not the allocator. It is out of allocator's control and has the similar performance impact as Active False Sharing (FIX ME: cite local) if these threads, who are sharing the same cache-line, start reading/writing the given objects simultanously.
     64                        /subsubsubsection External Fragmentation
     65                        External fragmentation is the free bits of memory between or around chunks of memory that are currently in-use of the application. Segmentation in memory due to application's usage pattern causes external fragmentation. The memory which is part of external fragmentation is completely free as it is neither used by allocator's internal book-keeping nor by the application. Ideally, an allocator should return a segment of memory back to the OS as soon as application frees it. But, this is not always the case. Allocators get memory from OS in one of the two ways.
     66
     67                        \begin{itemize}
     68                        \item
     69                        MMap: an allocator can ask OS for whole pages in mmap area. Then, the allocator segments the page internally and fulfills application's request.
     70                        \item
     71                        Heap: an allocator can ask OS for memory in heap area using system calls such as sbrk. Heap are grows downwards and shrinks upwards.
     72                        \begin{itemize}
     73
     74                        If an allocator uses mmap area, it can only return extra memory back to OS if the whole page is free i.e. no chunk on the page is in-use of the application. Even if one chunk on the whole page is currently in-use of the application, the allocator has to retain the whole page.
     75
     76                        If an allocator uses the heap area, it can only return the continous free memory at the end of the heap area that is currently in allocator's possession as heap area shrinks upwards. If there are free bits of memory in-between chunks of memory that are currently in-use of the application, the allocator can not return these free bits.
     77
     78*** FIX ME: Insert a figure of above scenrio with explanation
     79
     80                        Even if the entire heap area is free except one small chunk at the end of heap area that is being used by the application, the allocator cannot return the free heap area back to the OS as it is not a continous region at the end of heap area.
     81
     82*** FIX ME: Insert a figure of above scenrio with explanation
     83
     84                        Such scenerios cause external fragmentation but it is out of the allocator's control and depend on application's usage pattern.
     85
     86                /subsubsection Internal Memory Management
     87                Allocators such as je-malloc (FIX ME: insert reference) pro-actively get some memory from the OS and divide it into chunks of certain sizes that can be used in-future to fulfill application's request. This causes memory overhead as these chunks are made before application's request. There is also the possibility that an application may not even request memory of these sizes during their whole life-time.
     88
     89*** FIX ME: Insert a figure of above scenrio with explanation
     90
     91                Allocators such as rp-malloc (FIX ME: insert reference) maintain lists or blocks of sized memory segments that is freed by the application for future use. These lists are maintained without any guarantee that application will even request these sizes again.
     92
     93                Such tactics are usually used to gain speed as allocator will not have to get raw memory from OS and manage it at the time of application's request but they do cause memory overhead.
     94
     95        Fragmentation and managed sized chunks of free memory can lead to Heap Blowup as the allocator may not be able to use the fragments or sized free chunks of free memory to fulfill application's requests of other sizes.
     96
     97        /subsection Speed
     98        When it comes to performance evaluation of any piece of software, its runtime is usually the first thing that is evaluated. The same is true for memory allocators but, in case of memory allocators, speed does not only mean the runtime of memory allocator's routines but there are other factors too.
     99
     100                /subsubsection Runtime Speed
     101                Low runtime is the main goal of a memory allocator when it comes it proving its speed. Runtime is the time that it takes for a routine of memory allocator to complete its execution. As mentioned in (FIX ME: refernce to routines' list), there four basic routines that are used in memory allocation. Ideally, each routine of a memory allocator should be fast. Some memory allocator designs use pro-active measures (FIX ME: local refernce) to gain speed when allocating some memory to the application. Some memory allocators do memory allocation faster than memory freeing (FIX ME: graph refernce) while others show similar speed whether memory is allocated or freed.
     102
     103                /subsubsection Memory Access Speed
     104                Runtime speed is not the only speed matrix in memory allocators. The memory that a memory allocator has allocated to the application also needs to be accessible as quick as possible. The application should be able to read/write allocated memory quickly. The allocation method of a memory allocator may introduce some delays when it comes to memory access speed, which is specially important in concurrent applications. Ideally, a memory allocator should allocate all memory on a cache-line to only one thread and no cache-line should be shared among multiple threads. If a memory allocator allocates memory to multple threads on a same cache line, then cache may get invalidated more frequesntly when two different threads running on two different processes will try to read/write the same memory region. On the other hand, if one cache-line is used by only one thread then the cache may get invalidated less frequently. This sharing of one cache-line among multiple threads is called false sharing (FIX ME: cite wasik).
     105
     106                        /subsubsubsection Active False Sharing
     107                        Active false sharing is the sharing of one cache-line among multiple threads that is caused by memory allocator. It happens when two threads request memory from memory allocator and the allocator allocates memory to both of them on the same cache-line. After that, if the threads are running on different processes who have their own caches and both threads start reading/writing the allocated memory simultanously, their caches will start getting invalidated every time the other thread writes something to the memory. This will cause the application to slow down as the process has to load cache much more frequently.
     108
     109*** FIX ME: Insert a figure of above scenrio with explanation
     110
     111                        /subsubsubsection Passive False Sharing
     112                        Passive false sharing is the kind of false sharing which is caused by the application and not the memory allocator. The memory allocator may preservce passive false sharing in future instead of eradicating it. But, passive false sharing is initiated by the application.
     113
     114                                /subsubsubsubsection Program Induced Passive False Sharing
     115                                Program induced false sharing is completely out of memory allocator's control and is purely caused by the application. When a thread in the application creates multiple objects in the dynamic area and allocator allocates memory for these objects on the same cache-line as the objects are created by the same thread. Passive false sharing will occur if this thread passes one of these objects to another thread but it retains the rest of these objects or it passes some/all of the remaining objects to some third thread(s). Now, one cache-line is shared among multiple threads but it is caused by the application and not the allocator. It is out of allocator's control and has the similar performance impact as Active False Sharing (FIX ME: cite local) if these threads, who are sharing the same cache-line, start reading/writing the given objects simultanously.
    121116
    122117*** FIX ME: Insert a figure of above scenrio 1 with explanation
    … …  
    124119*** FIX ME: Insert a figure of above scenrio 2 with explanation
    125120
    126 \subparagraph{Program Induced Allocator Preserved Passive False Sharing}
    127 Program induced allocator preserved passive false sharing is another interesting case of passive false sharing. Both the application and the allocator are partially responsible for it. It starts the same as Program Induced False Sharing (FIX ME: cite local). Once, an application thread has created multiple dynamic objects on the same cache-line and ditributed these objects among multiple threads causing sharing of one cache-line among multiple threads (Program Induced Passive False Sharing). This kind of false sharing occurs when one of these threads, which got the object on the shared cache-line, frees the passed object then re-allocates another object but the allocator returns the same object (on the shared cache-line) that this thread just freed. Although, the application caused the false sharing to happen in the frst place however, to prevent furthur false sharing, the allocator should have returned the new object on some other cache-line which is only shared by the allocating thread. When it comes to performnce impact, this passive false sharing will slow down the application just like any other kind of false sharing if the threads sharing the cache-line start reading/writing the objects simultanously.
    128 
     121                                /subsubsubsubsection Program Induced Allocator Preserved Passive False Sharing
     122                                Program induced allocator preserved passive false sharing is another interesting case of passive false sharing. Both the application and the allocator are partially responsible for it. It starts the same as Program Induced False Sharing (FIX ME: cite local). Once, an application thread has created multiple dynamic objects on the same cache-line and ditributed these objects among multiple threads causing sharing of one cache-line among multiple threads (Program Induced Passive False Sharing). This kind of false sharing occurs when one of these threads, which got the object on the shared cache-line, frees the passed object then re-allocates another object but the allocator returns the same object (on the shared cache-line) that this thread just freed. Although, the application caused the false sharing to happen in the frst place however, to prevent furthur false sharing, the allocator should have returned the new object on some other cache-line which is only shared by the allocating thread. When it comes to performnce impact, this passive false sharing will slow down the application just like any other kind of false sharing if the threads sharing the cache-line start reading/writing the objects simultanously.
    129123
    130124*** FIX ME: Insert a figure of above scenrio with explanation
    … …  
    136130%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    137131
    138 \section{Micro Benchmark Suite}
     132\section Micro Benchmark Suite
    139133The 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.
    140134
    … …  
    145139*** FIX ME: Add knobs items after finalize
    146140
    147 \subsection{Memory Benchmark}
    148 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. Finally, 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.
    149 *** FIX ME: Insert a figure of above benchmark with description
    150 
    151 \subsubsection{Relevant Knobs}
    152 *** FIX ME: Insert Relevant Knobs
    153 
    154 \subsection{Speed Benchmark}
    155 Speed benchmark calculates the runtime speed of an allocator's functions (FIX ME: cite allocator routines). It does by measuring the runtime of allocator routines in two different ways.
    156 
    157 \subsubsection{Speed Time}
    158 The time method does a certain amount of work by calling each routine of the allocator (FIX ME: cite allocator routines) a specific time. It calculates the total time it took to perform this workload. Then, it divides the time it took by the workload and calculates the average time taken by the allocator's routine.
    159 *** FIX ME: Insert a figure of above benchmark with description
    160 
    161 \paragraph{Relevant Knobs}
    162 *** FIX ME: Insert Relevant Knobs
    163 
    164 \subsubsection{Speed Workload}
    165 The worload method uses the opposite approach. It calls the allocator's routines for a specific amount of time and measures how much work was done during that time. Then, similar to the time method, it divides the time by the workload done during that time and calculates the average time taken by the allocator's routine.
    166 *** FIX ME: Insert a figure of above benchmark with description
    167 
    168 \paragraph{Relevant Knobs}
    169 *** FIX ME: Insert Relevant Knobs
    170 
    171 \subsection{Cache Scratch}
    172 Cache Scratch benchmark measures program induced allocator preserved passive false sharing (FIX ME CITE) in an allocator. It does so in two ways.
    173 
    174 \subsubsection{Cache Scratch Time}
    175 Cache Scratch Time allocates dynamic objects. Then, it benchmarks program induced allocator preserved passive false sharing (FIX ME CITE) in an allocator by measuring the time it takes to read/write these objects.
    176 *** FIX ME: Insert a figure of above benchmark with description
    177 
    178 \paragraph{Relevant Knobs}
    179 *** FIX ME: Insert Relevant Knobs
    180 
    181 \subsubsection{Cache Scratch Layout}
    182 Cache Scratch Layout also allocates dynamic objects. Then, it benchmarks program induced allocator preserved passive false sharing (FIX ME CITE) by using heap addresses returned by the allocator. It calculates how many objects were allocated to different threads on the same cache line.
    183 *** FIX ME: Insert a figure of above benchmark with description
    184 
    185 \paragraph{Relevant Knobs}
    186 *** FIX ME: Insert Relevant Knobs
    187 
    188 \subsection{Cache Thrash}
    189 Cache Thrash benchmark measures allocator induced passive false sharing (FIX ME CITE) in an allocator. It also does so in two ways.
    190 
    191 \subsubsection{Cache Thrash Time}
    192 Cache Thrash Time allocates dynamic objects. Then, it benchmarks allocator induced false sharing (FIX ME CITE) in an allocator by measuring the time it takes to read/write these objects.
    193 *** FIX ME: Insert a figure of above benchmark with description
    194 
    195 \paragraph{Relevant Knobs}
    196 *** FIX ME: Insert Relevant Knobs
    197 
    198 \subsubsection{Cache Thrash Layout}
    199 Cache Thrash Layout also allocates dynamic objects. Then, it benchmarks allocator induced false sharing (FIX ME CITE) by using heap addresses returned by the allocator. It calculates how many objects were allocated to different threads on the same cache line.
    200 *** FIX ME: Insert a figure of above benchmark with description
    201 
    202 \paragraph{Relevant Knobs}
    203 *** FIX ME: Insert Relevant Knobs
    204 
    205 \section{Results}
     141        /subsection Memory Benchmark
     142        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. Finally, 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.
     143        *** FIX ME: Insert a figure of above benchmark with description
     144
     145                /subsubsection Relevant Knobs
     146                *** FIX ME: Insert Relevant Knobs
     147
     148        /subsection Speed Benchmark
     149        Speed benchmark calculates the runtime speed of an allocator's functions (FIX ME: cite allocator routines). It does by measuring the runtime of allocator routines in two different ways.
     150
     151                /subsubsection Speed Time
     152                The time method does a certain amount of work by calling each routine of the allocator (FIX ME: cite allocator routines) a specific time. It calculates the total time it took to perform this workload. Then, it divides the time it took by the workload and calculates the average time taken by the allocator's routine.
     153                *** FIX ME: Insert a figure of above benchmark with description
     154
     155                        /subsubsubsection Relevant Knobs
     156                        *** FIX ME: Insert Relevant Knobs
     157
     158                /subsubsection Speed Workload
     159                The worload method uses the opposite approach. It calls the allocator's routines for a specific amount of time and measures how much work was done during that time. Then, similar to the time method, it divides the time by the workload done during that time and calculates the average time taken by the allocator's routine.
     160                *** FIX ME: Insert a figure of above benchmark with description
     161
     162                        /subsubsubsection Relevant Knobs
     163                        *** FIX ME: Insert Relevant Knobs
     164
     165        /subsection Cache Scratch
     166        Cache Scratch benchmark measures program induced allocator preserved passive false sharing (FIX ME CITE) in an allocator. It does so in two ways.
     167
     168                /subsubsection Cache Scratch Time
     169                Cache Scratch Time allocates dynamic objects. Then, it benchmarks program induced allocator preserved passive false sharing (FIX ME CITE) in an allocator by measuring the time it takes to read/write these objects.
     170                *** FIX ME: Insert a figure of above benchmark with description
     171
     172                        /subsubsubsection Relevant Knobs
     173                        *** FIX ME: Insert Relevant Knobs
     174
     175                /subsubsection Cache Scratch Layout
     176                Cache Scratch Layout also allocates dynamic objects. Then, it benchmarks program induced allocator preserved passive false sharing (FIX ME CITE) by using heap addresses returned by the allocator. It calculates how many objects were allocated to different threads on the same cache line.
     177                *** FIX ME: Insert a figure of above benchmark with description
     178
     179                        /subsubsubsection Relevant Knobs
     180                        *** FIX ME: Insert Relevant Knobs
     181
     182        /subsection Cache Thrash
     183        Cache Thrash benchmark measures allocator induced passive false sharing (FIX ME CITE) in an allocator. It also does so in two ways.
     184
     185                /subsubsection Cache Thrash Time
     186                Cache Thrash Time allocates dynamic objects. Then, it benchmarks allocator induced false sharing (FIX ME CITE) in an allocator by measuring the time it takes to read/write these objects.
     187                *** FIX ME: Insert a figure of above benchmark with description
     188
     189                        /subsubsubsection Relevant Knobs
     190                        *** FIX ME: Insert Relevant Knobs
     191
     192                /subsubsection Cache Thrash Layout
     193                Cache Thrash Layout also allocates dynamic objects. Then, it benchmarks allocator induced false sharing (FIX ME CITE) by using heap addresses returned by the allocator. It calculates how many objects were allocated to different threads on the same cache line.
     194                *** FIX ME: Insert a figure of above benchmark with description
     195
     196                        /subsubsubsection Relevant Knobs
     197                        *** FIX ME: Insert Relevant Knobs
     198
     199/section Results
    206200*** FIX ME: add configuration details of memory allocators
    207201
    208 \subsection{Memory Benchmark}
    209 
    210 \subsubsection{Relevant Knobs}
    211 
    212 \subsection{Speed Benchmark}
    213 
    214 \subsubsection{Speed Time}
    215 
    216 \paragraph{Relevant Knobs}
    217 
    218 \subsubsection{Speed Workload}
    219 
    220 \paragraph{Relevant Knobs}
    221 
    222 \subsection{Cache Scratch}
    223 
    224 \subsubsection{Cache Scratch Time}
    225 
    226 \paragraph{Relevant Knobs}
    227 
    228 \subsubsection{Cache Scratch Layout}
    229 
    230 \paragraph{Relevant Knobs}
    231 
    232 \subsection{Cache Thrash}
    233 
    234 \subsubsection{Cache Thrash Time}
    235 
    236 \paragraph{Relevant Knobs}
    237 
    238 \subsubsection{Cache Thrash Layout}
    239 
    240 \paragraph{Relevant Knobs}
     202        /subsection Memory Benchmark
     203
     204                /subsubsection Relevant Knobs
     205
     206        /subsection Speed Benchmark
     207
     208                /subsubsection Speed Time
     209
     210                        /subsubsubsection Relevant Knobs
     211
     212                /subsubsection Speed Workload
     213
     214                        /subsubsubsection Relevant Knobs
     215
     216        /subsection Cache Scratch
     217
     218                /subsubsection Cache Scratch Time
     219
     220                        /subsubsubsection Relevant Knobs
     221
     222                /subsubsection Cache Scratch Layout
     223
     224                        /subsubsubsection Relevant Knobs
     225
     226        /subsection Cache Thrash
     227
     228                /subsubsection Cache Thrash Time
     229
     230                        /subsubsubsection Relevant Knobs
     231
     232                /subsubsection Cache Thrash Layout
     233
     234                        /subsubsubsection Relevant Knobs

  • doc/theses/mubeen_zulfiqar_MMath/uw-ethesis.tex

    rdcbfcbc r07b4970  
    165165% cfa macros used in the document
    166166\input{common}
    167 %\usepackageinput{common}
    168167\CFAStyle                                               % CFA code-style for all languages
    169 \lstset{basicstyle=\linespread{0.9}\tt}                 % CFA typewriter font
     168\lstset{language=CFA,basicstyle=\linespread{0.9}\tt}    % CFA default language
    170169\newcommand{\PAB}[1]{{\color{red}PAB: #1}}
    171170
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