From bd23ae138ec7cc861cb1d3c876e66662292a925a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Constantin=20F=C3=BCrst?= Date: Thu, 8 Feb 2024 17:45:28 +0100 Subject: [PATCH 01/12] finalize plotter script and add timing results --- qdp_project/plots/plot-timing-cacheaccess.pdf | Bin 0 -> 16740 bytes .../plots/plot-timing-distprefetch.pdf | Bin 0 -> 13761 bytes qdp_project/plots/plot-timing-dram.pdf | Bin 0 -> 12334 bytes qdp_project/plots/plot-timing-hbm.pdf | Bin 0 -> 13080 bytes qdp_project/plots/plot-timing-prefetch.pdf | Bin 0 -> 14097 bytes qdp_project/plotter.py | 40 +++++++++--------- 6 files changed, 20 insertions(+), 20 deletions(-) create mode 100644 qdp_project/plots/plot-timing-cacheaccess.pdf create mode 100644 qdp_project/plots/plot-timing-distprefetch.pdf create mode 100644 qdp_project/plots/plot-timing-dram.pdf create mode 100644 qdp_project/plots/plot-timing-hbm.pdf create mode 100644 qdp_project/plots/plot-timing-prefetch.pdf diff --git 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z@7LX`7974IbF#!&XgZ?a_BCP8Gs0Y6nimU;>0CnP*LeU3M zCw>-+u59Q8s1LsukS@wKFbAj`KMTk?7LEbd5TF&*0V)Nj0IUh97l60v z0wP|Y#tlk&p8`z1;YomHqs-$DdL2s31JLjFmM0Wolmhbto~8AcH()xz76fnrfGwch z1w9b3Je2l^V?u4PM{!=1Lm>bq5ej5gQF@V}fH9@avhJP$KerACJQjKb=Y|K|z)J?q z+j^gMF9)2RQ$~P}4^SD(T>hXx1flUv>DmlK!T*>~9s~Hc&Hd!TM|abF z3i5yOrU1Oi-P}(B`KRtGV6lJpQ}|OSaY%5N@;hE2I{p)e2KE7)`{BTZ_20$7ZfQJ4jj7-G4dlHD0PuI-cgMfVru%|Hf@c|7P#d7O=yZ8Vh1?2*;Y$P(CNT!$$ Q1QLgnhlq=7AJl>T52*+8r2qf` literal 0 HcmV?d00001 diff --git a/qdp_project/plotter.py b/qdp_project/plotter.py index 84afcd6..fcf7e85 100644 --- a/qdp_project/plotter.py +++ b/qdp_project/plotter.py @@ -5,8 +5,10 @@ import seaborn as sns import matplotlib.pyplot as plt output_path = "./plots" -prefetch_result = "./evaluation-results/qdp-xeonmax-prefetch-tca2-tcb1-tcj1-tmul8-wl4294967296-cs16777216.csv" -dram_result = "./evaluation-results/qdp-xeonmax-dram-tca2-tcb0-tcj1-tmul8-wl4294967296-cs2097152.csv" +hbm_result = "./evaluation-results/baseline/current-hbm/qdp-xeonmax-hbm-tca2-tcb0-tcj1-tmul16-wl4294967296-cs2097152.csv" +dram_result = "./evaluation-results/baseline/current-dram/qdp-xeonmax-dram-tca2-tcb0-tcj1-tmul16-wl4294967296-cs2097152.csv" +prefetch_result = "./evaluation-results/outofcacheallocation/qdp-xeonmax-prefetch-tca2-tcb1-tcj1-tmul16-wl4294967296-cs8388608.csv" +distprefetch_result = "./evaluation-results/distprefetch/qdp-xeonmax-distprefetch-tca1-tcb1-tcj1-tmul32-wl4294967296-cs8388608.csv" tt_name = "rt-ns" function_names = [ "scana-run", "scanb-run", "aggrj-run" ] @@ -14,30 +16,26 @@ fn_nice = [ "Scan A, Filter", "Scan B, Prefetch", "Aggregate, Project + Sum" ] def read_timings_from_csv(fname) -> tuple[list[float], list[str]]: t = {} - total_time = 0 - # Read data from CSV file + row_count = 0 + with open(fname, newline='') as csvfile: reader = csv.DictReader(csvfile, delimiter=';') for row in reader: - total_time += int(row[tt_name]) + row_count = row_count + 1 for i in range(len(function_names)): t[fn_nice[i]] = t.get(fn_nice[i], 0) + int(row[function_names[i]]) - t = {key: value * 100 / total_time for key, value in t.items() if value != 0} - - total = sum(list(t.values())) - if total < 100.0: - t["Waiting / Other"] = 100.0 - total + t = {key: value / (1000 * 1000 * row_count) for key, value in t.items() if value != 0} return list(t.values()), list(t.keys()) def get_data_prefetch_cache_access() -> tuple[list[float], list[str]]: - total = 0.3 - data = [ 0.07, 0.19, 0.04 ] + total = 0.47 + data = [ 0.01, 0.01, 0.04, 0.42 ] data = [ x * 100 / total for x in data ] - keys = ["numa_alloc_onnode", "dml::make_mem_move_task", "dml::hardware_device::submit"] + keys = ["Cache::GetCacheNode", "Cache::Access Itself", "dml::hardware_device::submit", "dml::make_mem_move_task (operator new)"] return data,keys @@ -45,13 +43,13 @@ def get_data_prefetch_total() -> tuple[list[float], list[str]]: return read_timings_from_csv(prefetch_result) def get_data_dram_total() -> tuple[list[float], list[str]]: - return read_timings_from_csv(dram_result) + return # loops over all possible configuration combinations and calls # process_file_to_dataset for them in order to build a dataframe # which is then displayed and saved -def main(data: tuple[list[float], list[str]], fname): - palette_color = sns.color_palette('mako') +def main(data: tuple[list[float], list[str]], fname, unit): + palette_color = sns.color_palette('mako_r') fig, ax = plt.subplots(figsize=(6, 3), subplot_kw=dict(aspect="equal")) wedges, texts = ax.pie(data[0], wedgeprops=dict(width=0.5), startangle=-40, colors=palette_color) @@ -66,12 +64,14 @@ def main(data: tuple[list[float], list[str]], fname): horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))] connectionstyle = f"angle,angleA=0,angleB={ang}" kw["arrowprops"].update({"connectionstyle": connectionstyle}) - ax.annotate(f"{data[1][i]} - {data[0][i]:2.1f}%", xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y), horizontalalignment=horizontalalignment, **kw) + ax.annotate(f"{data[1][i]} - {data[0][i]:2.2f} {unit}", xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y), horizontalalignment=horizontalalignment, **kw) fig.savefig(os.path.join(output_path, fname), bbox_inches='tight') if __name__ == "__main__": - main(get_data_prefetch_cache_access(), "plot-timing-prefetch-cacheaccess.pdf") - main(get_data_prefetch_total(), "plot-timing-prefetch-totalexec.pdf") - main(get_data_dram_total(), "plot-timing-dram-totalexec.pdf") + main(get_data_prefetch_cache_access(), "plot-timing-cacheaccess.pdf", "%") + main(read_timings_from_csv(prefetch_result), "plot-timing-prefetch.pdf", "ms") + main(read_timings_from_csv(distprefetch_result), "plot-timing-distprefetch.pdf", "ms") + main(read_timings_from_csv(dram_result), "plot-timing-dram.pdf", "ms") + main(read_timings_from_csv(hbm_result), "plot-timing-hbm.pdf", "ms") From 563f01ba424bbcba7e8ab5f13873c747c65992cc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Constantin=20F=C3=BCrst?= Date: Thu, 8 Feb 2024 18:39:17 +0100 Subject: [PATCH 02/12] finalize chapter 5 and detail the challenges of cache application to qdp --- thesis/content/50_implementation.tex | 32 ++++++++++++---------------- 1 file changed, 14 insertions(+), 18 deletions(-) diff --git a/thesis/content/50_implementation.tex b/thesis/content/50_implementation.tex index 1a61149..b285651 100644 --- a/thesis/content/50_implementation.tex +++ b/thesis/content/50_implementation.tex @@ -20,7 +20,7 @@ % nichts zu suchen, auch nicht im Anhang, sondern gehören auf Rechner, % auf denen man sie sich ansehen kann. -In this chapter, we concentrate on specific implementation details, offering an in-depth view of how the design promises outlined in Chapter \ref{chap:design} are realized. Firstly, we delve into the usage of locking and atomics to achieve thread safety. Subsequently, we provide an example of the policy functions alluded to in Section \ref{sec:design:accel-usage}. Finally, we apply the cache to \glsentrylong{qdp}. \par +In this chapter, we concentrate on specific implementation details, offering an in-depth view of how the design promises outlined in Chapter \ref{chap:design} are realized. Firstly, we delve into the usage of locking and atomics to achieve thread safety. Finally, we apply the cache to \glsentrylong{qdp}, detailing the policies mentioned in Section \ref{sec:design:accel-usage} and presenting solutions for the challenges encountered. \par \section{Locking and Usage of Atomics} @@ -53,9 +53,9 @@ Therefore, the decision was made to implement atomic reference counting for \tex \label{fig:impl-cachedata-threadseq-waitoncompletion} \end{figure} -Due to the possibility of access by multiple threads, the implementation of \texttt{CacheData::WaitOnCompletion} proved to be challenging. In the first implementation, a thread would check if the handlers are available and atomically wait \cite{cppreference:atomic-wait} on a value change from nullptr, if they are not. As the handlers are only available after submission, a situation could arise where only one copy of \texttt{CacheData} is capable of actually waiting on them. \par +Due to the possibility of access by multiple threads, the implementation of \texttt{CacheData::WaitOnCompletion} proved to be challenging. In the first implementation, a thread would check if the handlers are available and atomically wait \cite{cppreference:atomic-wait} on a value change from \texttt{nullptr}, if they are not. As the handlers are only available after submission, a situation could arise where only one copy of \texttt{CacheData} is capable of actually waiting on them. \par -To illustrate this, an exemplary scenario is used, as seen in the sequence diagram Figure \ref{fig:impl-cachedata-threadseq-waitoncompletion}. Assume that three threads \(T_1\), \(T_2\) and \(T_3\) wish to access the same resource. \(T_1\) is the first to call \texttt{CacheData::Access} and therefore adds it to the cache state and will perform the work submission. Before \(T_1\) may submit the work, it is interrupted and \(T_2\) and \(T_3\) obtain access to the incomplete \texttt{CacheData} on which they wait, causing them to see a nullptr for the handlers but invalid cache pointer, leading to atomic wait on the cache pointer (marked blue lines in Figure \ref{fig:impl-cachedata-threadseq-waitoncompletion}). \(T_1\) submits the work and sets the handlers (marked red lines in Figure \ref{fig:impl-cachedata-threadseq-waitoncompletion}), while \(T_2\) and \(T_3\) continue to wait. Therefore, only \(T_1\) can trigger the waiting and is therefore capable of keeping \(T_2\) and \(T_3\) from progressing. This is undesirable as it can lead to deadlocking if by some reason \(T_1\) does not wait and at the very least may lead to unnecessary delay for \(T_2\) and \(T_3\) if \(T_1\) does not wait immediately. \par +To illustrate this, an exemplary scenario is used, as seen in the sequence diagram Figure \ref{fig:impl-cachedata-threadseq-waitoncompletion}. Assume that three threads \(T_1\), \(T_2\) and \(T_3\) wish to access the same resource. \(T_1\) is the first to call \texttt{CacheData::Access} and therefore adds it to the cache state and will perform the work submission. Before \(T_1\) may submit the work, it is interrupted and \(T_2\) and \(T_3\) obtain access to the incomplete \texttt{CacheData} on which they wait, causing them to see a \texttt{nullptr} for the handlers but invalid cache pointer, leading to atomic wait on the cache pointer (marked blue lines in Figure \ref{fig:impl-cachedata-threadseq-waitoncompletion}). \(T_1\) submits the work and sets the handlers (marked red lines in Figure \ref{fig:impl-cachedata-threadseq-waitoncompletion}), while \(T_2\) and \(T_3\) continue to wait. Therefore, only \(T_1\) can trigger the waiting and is therefore capable of keeping \(T_2\) and \(T_3\) from progressing. This is undesirable as it can lead to deadlocking if by some reason \(T_1\) does not wait and at the very least may lead to unnecessary delay for \(T_2\) and \(T_3\) if \(T_1\) does not wait immediately. \par \begin{figure}[h!tb] \centering @@ -64,14 +64,14 @@ To illustrate this, an exemplary scenario is used, as seen in the sequence diagr \label{fig:impl-cachedata-waitoncompletion} \end{figure} -As a solution for this, a more intricate implementation is required. When waiting, the threads now immediately check whether the cache pointer contains a valid value and return if it does, as nothing has to be waited for in this case. We will use the same example as before to illustrate the second part of the waiting procedure. Both \(T_2\) and \(T_3\) arrive in this latter section as the cache was invalid at the point in time when waiting was called for. They now atomically wait on the handlers-pointer to change, instead of doing it the other way around as before. Now when \(T_1\) supplies the handlers, it also uses \texttt{std::atomic::notify\_one} \cite{cppreference:atomic-notify-one} to wake at least one thread waiting on value change of the handlers-pointer, if there are any. Through this the exclusion that was observable in the first implementation is already avoided. If nobody is waiting, then the handlers will be set to a valid pointer and a thread may pass the atomic wait instruction later on. Following this wait, the handlers-pointer is atomically exchanged \cite{cppreference:atomic-exchange} with nullptr, invalidating it. Each thread again checks whether it has received a valid local pointer to the handlers from the exchange. If it has then the atomic operation guarantees that is now in sole possession of the pointer. The owning thread is tasked with actually waiting. All other threads will now regress and call \texttt{CacheData::WaitOnCompletion} again. The solo thread may proceed to wait on the handlers and should update the cache pointer. \par +As a solution for this, a more intricate implementation is required. When waiting, the threads now immediately check whether the cache pointer contains a valid value and return if it does, as nothing has to be waited for in this case. We will use the same example as before to illustrate the second part of the waiting procedure. Both \(T_2\) and \(T_3\) arrive in this latter section as the cache was invalid at the point in time when waiting was called for. They now atomically wait on the handlers-pointer to change, instead of doing it the other way around as before. Now when \(T_1\) supplies the handlers, it also uses \texttt{std::atomic::notify\_one} \cite{cppreference:atomic-notify-one} to wake at least one thread waiting on value change of the handlers-pointer, if there are any. Through this the exclusion that was observable in the first implementation is already avoided. If nobody is waiting, then the handlers will be set to a valid pointer and a thread may pass the atomic wait instruction later on. Following this wait, the handlers-pointer is atomically exchanged \cite{cppreference:atomic-exchange} with \texttt{nullptr}, invalidating it. Each thread again checks whether it has received a valid local pointer to the handlers from the exchange. If it has then the atomic operation guarantees that is now in sole possession of the pointer. The owning thread is tasked with actually waiting. All other threads will now regress and call \texttt{CacheData::WaitOnCompletion} again. The solo thread may proceed to wait on the handlers and should update the cache pointer. \par Additional cases must be considered for the latter implementation to be safe and free of deadlocks. We will now discuss these edge cases and their resolution. \par \subsubsection{Initial Invalid State} \label{subsubsec:impl:cdatomicity:initial-invalid-state} -We previously mentioned the possibly problematic situation where both the cache pointer and the handlers are not yet available for an instance in \texttt{CacheData}. This situation is avoided explicitly by the implementation due to waiting on the handlers being atomically updated from nullptr to valid. When the handlers will be set in the future by the thread calling \texttt{Cache::Access} first, progress is guaranteed. \par +We previously mentioned the possibly problematic situation where both the cache pointer and the handlers are not yet available for an instance in \texttt{CacheData}. This situation is avoided explicitly by the implementation due to waiting on the handlers being atomically updated from \texttt{nullptr} to valid. When the handlers will be set in the future by the thread calling \texttt{Cache::Access} first, progress is guaranteed. \par \subsubsection{Invalid State on Immediate Destruction} @@ -83,9 +83,9 @@ To circumvent this deadlock, the initial state of \texttt{CacheData} was modifie \subsubsection{Invalid State on Operation Failure} -\texttt{CacheData::WaitOnCompletion} first checks for a valid cache pointer and then waits on the handlers becoming valid. To process the handlers, the global atomic pointer is read into a local copy and then set to nullptr using \texttt{std::atomic::exchange}. During evaluation of the handlers completion states, an unsuccessful operation may be found. In this case, the cache memory region remains invalid and may therefore not be used. In this case, both the handlers and the cache pointer will be nullptr. This results in an invalid state, like the one discussed in Section \ref{subsubsec:impl:cdatomicity:initial-invalid-state}. \par +\texttt{CacheData::WaitOnCompletion} first checks for a valid cache pointer and then waits on the handlers becoming valid. To process the handlers, the global atomic pointer is read into a local copy and then set to \texttt{nullptr} using \texttt{std::atomic::exchange}. During evaluation of the handlers completion states, an unsuccessful operation may be found. In this case, the cache memory region remains invalid and may therefore not be used. In this case, both the handlers and the cache pointer will be \texttt{nullptr}. This results in an invalid state, like the one discussed in Section \ref{subsubsec:impl:cdatomicity:initial-invalid-state}. \par -In this invalid state, progress is not guaranteed by the measures set forth to handle the initial invalidity. The cache is still nullptr and as the handlers have already been set and processed, they will also be nullptr without the chance of them ever becoming valid. \par +In this invalid state, progress is not guaranteed by the measures set forth to handle the initial invalidity. The cache is still \texttt{nullptr} and as the handlers have already been set and processed, they will also be \texttt{nullptr} without the chance of them ever becoming valid. \par Edge case handling is introduced and the cache pointer is set to the source address, providing validity. \par @@ -93,27 +93,23 @@ Edge case handling is introduced and the cache pointer is set to the source addr The guarantee of \texttt{std::atomic::wait} to only wake up when the value has changed \cite{cppreference:atomic-wait} was found to be stronger than the promise of waking up all waiting threads with \texttt{std::atomic::notify\_all} \cite{cppreference:atomic-notify-all}. \par -As visible in Figure \ref{fig:impl-cachedata-waitoncompletion}, we wait while the handlers-pointer is nullptr, if the cache pointer is invalid. To exemplify we use the following scenario. Both \(T_1\) and \(T_2\) call \texttt{CacheData::WaitOnCompletion}, with \(T_1\) preceding \(T_2\). \(T_1\) exchanges the global handlers-pointer with nullptr, invalidating it. Before \(T_1\) can check the status of the handlers and update the cache pointer, \(T_2\) sees an invalid cache pointer and then waits for the handlers becoming available. \par +As visible in Figure \ref{fig:impl-cachedata-waitoncompletion}, we wait while the handlers-pointer is \texttt{nullptr}, if the cache pointer is invalid. To exemplify we use the following scenario. Both \(T_1\) and \(T_2\) call \texttt{CacheData::WaitOnCompletion}, with \(T_1\) preceding \(T_2\). \(T_1\) exchanges the global handlers-pointer with \texttt{nullptr}, invalidating it. Before \(T_1\) can check the status of the handlers and update the cache pointer, \(T_2\) sees an invalid cache pointer and then waits for the handlers becoming available. \par This has again caused a similar state of invalidity as the previous two Sections handled. As the handlers will not become available again due to being cleared by \(T_1\), the second consumer, \(T_2\), will now wait indefinitely. This missed update is commonly referred to as \enquote{ABA-Problem} for which multiple solutions exist. \par -One could use double-width atomic operations and introduce a counter which would allow resetting the pointer back to null while setting a flag indicating the exchange took place. The handlers-pointer would then be contained in a struct with this flag, allowing exchange with a composite of nullptr and flag-set. Other threads then would then wait on the struct changing from nullptr and flag-unset, allowing them to pass if either the flag is set or the handlers have become non-null. As standard C++ does not yet support the required operations, we chose to avoid the missed update differently. \cite{dwcas-cpp} \par +One could use double-width atomic operations and introduce a counter which would allow resetting the pointer back to null while setting a flag indicating the exchange took place. The handlers-pointer would then be contained in a struct with this flag, allowing exchange with a composite of \texttt{nullptr} and flag-set. Other threads then would then wait on the struct changing from \texttt{nullptr} and flag-unset, allowing them to pass if either the flag is set or the handlers have become non-null. As standard C++ does not yet support the required operations, we chose to avoid the missed update differently. \cite{dwcas-cpp} \par -The chosen solution for this is to not exchange the handlers-pointer with nullptr but with a second invalid value. We must determine a secondary invalid pointer for use in the exchange. Therefore, we introduce a new attribute, of the same type as the one pointed to by the handlers-pointer, to \texttt{Cache}. The \texttt{Cache} then shares it with each instance of \texttt{CacheData}, where it is then used in \texttt{CacheData::WaitOnCompletion}. \par +The chosen solution for this is to not exchange the handlers-pointer with \texttt{nullptr} but with a second invalid value. We must determine a secondary invalid pointer for use in the exchange. Therefore, we introduce a new attribute, of the same type as the one pointed to by the handlers-pointer, to \texttt{Cache}. The \texttt{Cache} then shares it with each instance of \texttt{CacheData}, where it is then used in \texttt{CacheData::WaitOnCompletion}. \par -This secondary value allows \(T_2\) to pass the wait, then perform the exchange of handlers itself. \(T_2\) then checks the local copy of the handlers-pointer for validity. The invalid state now includes both nullptr and the secondary invalid pointer chosen. With this, the deadlock is avoided and \(T_2\) will wait for \(T_1\) completing the processing of the handlers. \par - -\section{Accelerator Usage} - -After \ref{sec:design:accel-usage} the implementation of \texttt{Cache} provided leaves it up to the user to choose a caching and copy method policy which is accomplished through submitting function pointers at initialization of the \texttt{Cache}. In \ref{sec:state:setup-and-config} we configured our system to have separate \gls{numa:node}s for accessing \gls{hbm} which are assigned a \gls{numa:node}-ID by adding eight to the \gls{numa:node}s ID of the \gls{numa:node} that physically contains the \gls{hbm}. Therefore, given \gls{numa:node} 3 accesses some datum, the most efficient placement for the copy would be on \gls{numa:node} \(3 + 8 = 11\). As the \texttt{Cache} is intended for multithreaded usage, conserving accelerator resources is important, so that concurrent cache requests complete quickly. To get high per-copy performance while maintaining low usage, the Push-Pull method is selected as described in \ref{subsec:perf:datacopy} for larger copies, while small copies will be handled exclusively by the current node. We introduce this distinction by datum size due to the observations in Section \ref{subsec:perf:submitmethod} that with lower transfer sizes, the effect of submission cost is amplified. \par +This secondary value allows \(T_2\) to pass the wait, then perform the exchange of handlers itself. \(T_2\) then checks the local copy of the handlers-pointer for validity. The invalid state now includes both \texttt{nullptr} and the secondary invalid pointer chosen. With this, the deadlock is avoided and \(T_2\) will wait for \(T_1\) completing the processing of the handlers. \par \section{Application to \glsentrylong{qdp}} -Applying the \texttt{Cache} to \gls{qdp} is straightforward. We adapted the benchmarking code developed by Anna Bartuschka and André Berthold \cite{dimes-prefetching}, calling \texttt{Cache::Access} for both prefetching and cache access. \par +Applying the \texttt{Cache} to \gls{qdp} is a straightforward process. We adapted the benchmarking code developed by Anna Bartuschka and André Berthold \cite{dimes-prefetching}, invoking Cache::Access for both prefetching and cache access. Due to the high amount of smaller submissions, we decided to forego splitting of tasks unto multiple \gls{dsa} and instead distribute the copy tasks per thread in round-robin fashion to all available. This causes less delay due to submission cost which, as shown in Section \ref{subsec:perf:submitmethod}, rises with smaller tasks. The cache location is fixed to \gls{numa:node} 8, the \gls{hbm} accessor of \gls{numa:node} 0 to which the application will be bound and therefore exclusively run on. \par -During performance analysis of the developed \texttt{Cache}, we found that \gls{intel:dml} does not utilize interrupt-based completion signalling (Section \ref{subsubsec:state:completion-signal}), but instead busy-waits on the completion descriptor being updated. As this busy waiting costs CPU cycles, waiting on task completion is out of question, requiring code modifications which we now detail. \par +During the performance analysis of the developed \texttt{Cache}, we discovered that \gls{dml} does not utilize interrupt-based completion signaling (Section \ref{subsubsec:state:completion-signal}), but instead employs busy-waiting on the completion descriptor being updated. Given that this busy waiting incurs CPU cycles, waiting on task completion is deemed impractical, necessitating code modifications, which we elaborate on below. We extended \texttt{CacheData} and Cache to incorporate support for weak waiting. By introducing a flag configurable in Cache, all instances of \texttt{CacheData} created via \texttt{Cache::Access} will check only once whether the \gls{dsa} has completed processing \texttt{Cache} operation, and if not, return without updating the cache-pointer. Consequently, calls to \texttt{CacheData::GetDataLocation} may return \texttt{nullptr} even after waiting, placing the responsibility on the user to access the data through its source location. For applications prioritizing latency, \texttt{Cache::Access} offers the option for weak access. When activated, the function returns only existing instances of \texttt{CacheData}, thereby avoiding work submission to the \gls{dsa} if the address has not been previously cached or was flushed since the last access. \par -We extended \texttt{CacheData} and \texttt{Cache} to provide support for weak waiting. Through a flag configurable in \texttt{Cache}, all instances of \texttt{CacheData} created through \texttt{Cache::Access} will only check once whether the \gls{dsa} has completed processing the operation, and otherwise return without updating the cache-pointer. Calls to \texttt{CacheData::GetDataLocation} can therefore return nullptr, even after waiting. The user is then responsible to access the data through its source location. For latency-critical applications, \texttt{Cache::Access} provides the option for weak access. When set, the function will only return an existing instance of \texttt{CacheData} and therefore does not cause work submission to the \gls{dsa}. \par +Additionally, we observed inefficiencies stemming from page fault handling. Task execution time increases when page faults are handled by the \gls{dsa}, leading to cache misses. Consequently, our execution time becomes bound to that of \gls{dram}, as misses prompt a fallback to the data's source location. When page faults are handled by the CPU during allocation, these misses are avoided. However, the execution time of the first data access through the \texttt{Cache} significantly increases due to page fault handling. One potential solution entails bypassing the system's memory management by allocating a large memory block and implementing a custom memory management scheme. As memory allocation is a complex topic, we opted to delegate this responsibility to the user by mandating the provision of new- and free-like functions akin to the policy functions utilized for determining placement and task distribution. Consequently, the benchmark can pre-allocate the required memory blocks, trigger page mapping, and subsequently pass these regions to the \texttt{Cache}. \par %%% Local Variables: %%% TeX-master: "diplom" From d935f38010621f658a60e963099ee95a01315ec6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Constantin=20F=C3=BCrst?= Date: Thu, 8 Feb 2024 18:39:35 +0100 Subject: [PATCH 03/12] add some additional notes for writing evaluation chapter --- thesis/content/60_evaluation.tex | 2 ++ 1 file changed, 2 insertions(+) diff --git a/thesis/content/60_evaluation.tex b/thesis/content/60_evaluation.tex index 2967ca4..5802248 100644 --- a/thesis/content/60_evaluation.tex +++ b/thesis/content/60_evaluation.tex @@ -27,6 +27,8 @@ With this difficult scenario, we expect to spend time analysing runtime behaviou Consider using parts of flamegraph. Same speed as dram, even though allocation is performed in the timed region and not before. Mention dml performs busy waiting (cite dsa-paper 4.4 for use of interrupts mentioned in arch), optimization with weak wait. Mention optimization weak access for prefetching scenario. +Scan A is memory bound, therefore copying B from DRAM to HBM directly cuts into available bandwidth to A when both are located on the same node. Better performance when A and B are stored on different nodes. + \section{Observation and Discussion} %%% Local Variables: From 3e4122ad462ea55e25eb3fdbdb562b2c33b6a858 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Constantin=20F=C3=BCrst?= Date: Fri, 9 Feb 2024 14:53:34 +0100 Subject: [PATCH 04/12] reformulate abstract and intro slightly --- thesis/content/02_abstract.tex | 2 +- thesis/content/10_introduction.tex | 4 +--- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/thesis/content/02_abstract.tex b/thesis/content/02_abstract.tex index f805733..a651276 100644 --- a/thesis/content/02_abstract.tex +++ b/thesis/content/02_abstract.tex @@ -8,7 +8,7 @@ % geben (für irgendetwas müssen die Betreuer ja auch noch da % sein). -This bachelor's thesis explores the dynamic landscape of heterogeneous memory systems, characterized by advancements in main memory technologies such as Non-Volatile RAM (NVRAM), High Bandwidth Memory (HBM), and Remote Memory. These systems necessitate strategic decisions regarding data placement to optimize performance, requiring the movement of data across different storage tiers. Consequently, the responsibility for maintaining optimal data placement falls upon the CPU, resulting in a reduction of available cycles for computational tasks. In response to this challenge, Intel has introduced the Data Streaming Accelerator (DSA), which offloads data operations, offering a potential avenue for enhancing efficiency in data-intensive applications. The primary objective of this thesis is to provide a comprehensive analysis and characterization of the architecture and performance of the DSA, along with its application to a domain-specific prefetching methodology aimed at accelerating database queries within heterogeneous memory systems. +This bachelor's thesis explores the dynamic landscape of heterogeneous memory systems, characterized by advancements in main memory technologies such as Non-Volatile RAM (NVRAM), High Bandwidth Memory (HBM), and Remote Memory. Systems equipped with more than one type of main memory necessitate strategic decisions regarding data placement to take advantage of the properties of the different storage tiers. The responsibility for maintaining optimal data placement falls upon the CPU, resulting in a reduction of available cycles for computational tasks. In response to this challenge, Intel has introduced the Data Streaming Accelerator (DSA), which offloads data operations, offering a potential avenue for enhancing efficiency in data-intensive applications. The primary objective of this thesis is to provide a comprehensive analysis and characterization of the architecture and performance of the DSA, along with its application to a domain-specific prefetching methodology aimed at accelerating database queries within heterogeneous memory systems. %%% Local Variables: %%% TeX-master: "diplom" diff --git a/thesis/content/10_introduction.tex b/thesis/content/10_introduction.tex index 46c5a26..e43eacd 100644 --- a/thesis/content/10_introduction.tex +++ b/thesis/content/10_introduction.tex @@ -12,9 +12,7 @@ % den Rest der Arbeit. Meist braucht man mindestens 4 Seiten dafür, mehr % als 10 Seiten liest keiner. -The proliferation of various technologies, such as..., has ushered in a diverse landscape of systems characterized by varying tiers of main memory. Within these systems, the movement of data across memory classes becomes imperative to leverage the distinct properties offered by the available technologies. Traditionally tasked with managing data locality, the CPU faces an added burden in heterogeneous memory environments, thereby diminishing available processing cycles. To mitigate this strain on the CPU, certain Intel Server CPUs now feature the \glsentryfirst{dsa} \cite{intel:xeonbrief}. \par - -In response to these challenges, this thesis undertakes the intricate task of optimizing data movement operations. At the core of this endeavor lies the introduction of the \gls{dsa}, which plays a pivotal role in enhancing streaming data movement operations across diverse applications. A thorough understanding of the architecture and functionality of \gls{dsa} is essential in addressing the challenges posed by this new form of \gls{numa}. \par +The proliferation of various technologies, such as Non-Volatile RAM (NVRAM), High Bandwidth Memory (HBM), and Remote Memory, has ushered in a diverse landscape of systems characterized by varying tiers of main memory. Within these systems, the movement of data across memory classes becomes imperative to leverage the distinct properties offered by the available technologies. Traditionally tasked with managing data locality, the CPU faces an added burden in heterogeneous memory environments, thereby diminishing available processing cycles. To mitigate this strain on the CPU, certain Intel Server Processors now feature the \glsentryfirst{dsa}, to which certain data operations may be offloaded \cite{intel:xeonbrief}. With it, this thesis undertakes the challenge of optimizing data locality on \glsentrylong{numa}s. \par The primary objectives of this thesis are twofold. Firstly, it involves a comprehensive analysis and characterization of the Intel \gls{dsa} architecture. Secondly, the focus extends to the application of \gls{dsa} in the domain-specific context of \glsentryfirst{qdp} to accelerate database queries \cite{dimes-prefetching}. This thesis seeks to explore how \gls{dsa} can be strategically utilized to tackle the challenges posed by heterogeneous memory systems, offering insights into the integration of data streaming acceleration with intelligent prefetching. \par From 8a3c76c8e3a8a3133a8c16f593a572d88a70b2af Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Constantin=20F=C3=BCrst?= Date: Fri, 9 Feb 2024 14:53:50 +0100 Subject: [PATCH 05/12] introduce glossary entry for memory pressure --- thesis/content/50_implementation.tex | 2 +- thesis/own.gls | 5 +++++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/thesis/content/50_implementation.tex b/thesis/content/50_implementation.tex index b285651..434db59 100644 --- a/thesis/content/50_implementation.tex +++ b/thesis/content/50_implementation.tex @@ -28,7 +28,7 @@ The usage of locking and atomics has proven to be challenging. Their use is perf \subsection{Cache State Lock} \label{subsec:implementation:cache-state-lock} -To keep track of the current cache state the \texttt{Cache} will hold a reference to each currently existing \texttt{CacheData} instance. The reason for this is twofold: In Section \ref{sec:design:cache} we decided to keep elements in the cache until forced by memory pressure to remove them. Secondly in Section \ref{subsec:design:cache-entry-reuse} we decided to reuse one cache entry for multiple consumers. The second part requires access to the structure holding this reference to be thread safe when accessing and modifying the cache state in \texttt{Cache::Access}, \texttt{Cache::Flush} and \texttt{Cache::Clear}. The latter two both require unique locking, preventing other calls to \texttt{Cache} from making progress while the operation is being processed. For \texttt{Cache::Access} the use of locking depends upon the caches state. At first, only a shared lock is acquired for checking whether the given address already resides in cache, allowing other \texttt{Cache::Access}-operations to also perform this check. If no entry for the region is present, a unique lock is required as well when adding the newly created entry to cache. \par +To keep track of the current cache state the \texttt{Cache} will hold a reference to each currently existing \texttt{CacheData} instance. The reason for this is twofold: In Section \ref{sec:design:cache} we decided to keep elements in the cache until forced by \gls{mempress} to remove them. Secondly in Section \ref{subsec:design:cache-entry-reuse} we decided to reuse one cache entry for multiple consumers. The second part requires access to the structure holding this reference to be thread safe when accessing and modifying the cache state in \texttt{Cache::Access}, \texttt{Cache::Flush} and \texttt{Cache::Clear}. The latter two both require unique locking, preventing other calls to \texttt{Cache} from making progress while the operation is being processed. For \texttt{Cache::Access} the use of locking depends upon the caches state. At first, only a shared lock is acquired for checking whether the given address already resides in cache, allowing other \texttt{Cache::Access}-operations to also perform this check. If no entry for the region is present, a unique lock is required as well when adding the newly created entry to cache. \par A map-datastructure was chosen to represent the current cache state with the key being the memory address of the entry and as value the \texttt{CacheData} instance. As the caching policy is controlled by the user, one datum may be requested for caching in multiple locations. To accommodate this, one map is allocated for each available \glsentrylong{numa:node} of the system. This can be exploited to reduce lock contention by separately locking each \gls{numa:node}'s state instead of utilizing a global lock. This ensures that \texttt{Cache::Access} and the implicit \texttt{Cache::Flush} it may cause can not hinder progress of caching operations on other \gls{numa:node}s. Both \texttt{Cache::Clear} and a complete \texttt{Cache::Flush} as callable by the user will now iteratively perform their respective task per \gls{numa:node} state, also allowing other \gls{numa:node} to progress.\par diff --git a/thesis/own.gls b/thesis/own.gls index ef57d16..61f1d07 100644 --- a/thesis/own.gls +++ b/thesis/own.gls @@ -132,4 +132,9 @@ long={Query-driven Prefetching}, first={Query-driven Prefetching (QdP)}, description={... desc ...} +} + +\newglossaryentry{mempress}{ + name={memory pressure}, + description={... desc ...} } \ No newline at end of file From 3be81b6d970df3d8422668df59486e7633e2298e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Constantin=20F=C3=BCrst?= Date: Fri, 9 Feb 2024 15:08:32 +0100 Subject: [PATCH 06/12] use glossary entry intel:dml and not just dml --- thesis/content/50_implementation.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/thesis/content/50_implementation.tex b/thesis/content/50_implementation.tex index 434db59..62d8c65 100644 --- a/thesis/content/50_implementation.tex +++ b/thesis/content/50_implementation.tex @@ -107,7 +107,7 @@ This secondary value allows \(T_2\) to pass the wait, then perform the exchange Applying the \texttt{Cache} to \gls{qdp} is a straightforward process. We adapted the benchmarking code developed by Anna Bartuschka and André Berthold \cite{dimes-prefetching}, invoking Cache::Access for both prefetching and cache access. Due to the high amount of smaller submissions, we decided to forego splitting of tasks unto multiple \gls{dsa} and instead distribute the copy tasks per thread in round-robin fashion to all available. This causes less delay due to submission cost which, as shown in Section \ref{subsec:perf:submitmethod}, rises with smaller tasks. The cache location is fixed to \gls{numa:node} 8, the \gls{hbm} accessor of \gls{numa:node} 0 to which the application will be bound and therefore exclusively run on. \par -During the performance analysis of the developed \texttt{Cache}, we discovered that \gls{dml} does not utilize interrupt-based completion signaling (Section \ref{subsubsec:state:completion-signal}), but instead employs busy-waiting on the completion descriptor being updated. Given that this busy waiting incurs CPU cycles, waiting on task completion is deemed impractical, necessitating code modifications, which we elaborate on below. We extended \texttt{CacheData} and Cache to incorporate support for weak waiting. By introducing a flag configurable in Cache, all instances of \texttt{CacheData} created via \texttt{Cache::Access} will check only once whether the \gls{dsa} has completed processing \texttt{Cache} operation, and if not, return without updating the cache-pointer. Consequently, calls to \texttt{CacheData::GetDataLocation} may return \texttt{nullptr} even after waiting, placing the responsibility on the user to access the data through its source location. For applications prioritizing latency, \texttt{Cache::Access} offers the option for weak access. When activated, the function returns only existing instances of \texttt{CacheData}, thereby avoiding work submission to the \gls{dsa} if the address has not been previously cached or was flushed since the last access. \par +During the performance analysis of the developed \texttt{Cache}, we discovered that \gls{intel:dml} does not utilize interrupt-based completion signaling (Section \ref{subsubsec:state:completion-signal}), but instead employs busy-waiting on the completion descriptor being updated. Given that this busy waiting incurs CPU cycles, waiting on task completion is deemed impractical, necessitating code modifications, which we elaborate on below. We extended \texttt{CacheData} and Cache to incorporate support for weak waiting. By introducing a flag configurable in Cache, all instances of \texttt{CacheData} created via \texttt{Cache::Access} will check only once whether the \gls{dsa} has completed processing \texttt{Cache} operation, and if not, return without updating the cache-pointer. Consequently, calls to \texttt{CacheData::GetDataLocation} may return \texttt{nullptr} even after waiting, placing the responsibility on the user to access the data through its source location. For applications prioritizing latency, \texttt{Cache::Access} offers the option for weak access. When activated, the function returns only existing instances of \texttt{CacheData}, thereby avoiding work submission to the \gls{dsa} if the address has not been previously cached or was flushed since the last access. \par Additionally, we observed inefficiencies stemming from page fault handling. Task execution time increases when page faults are handled by the \gls{dsa}, leading to cache misses. Consequently, our execution time becomes bound to that of \gls{dram}, as misses prompt a fallback to the data's source location. When page faults are handled by the CPU during allocation, these misses are avoided. However, the execution time of the first data access through the \texttt{Cache} significantly increases due to page fault handling. One potential solution entails bypassing the system's memory management by allocating a large memory block and implementing a custom memory management scheme. As memory allocation is a complex topic, we opted to delegate this responsibility to the user by mandating the provision of new- and free-like functions akin to the policy functions utilized for determining placement and task distribution. Consequently, the benchmark can pre-allocate the required memory blocks, trigger page mapping, and subsequently pass these regions to the \texttt{Cache}. \par From 89887b6cad810102f1504948b0a2c0fe671d6332 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Constantin=20F=C3=BCrst?= Date: Fri, 9 Feb 2024 15:09:07 +0100 Subject: [PATCH 07/12] slight modification of the intro, making it fit on one page --- thesis/bachelor.pdf | Bin 1308780 -> 1310604 bytes thesis/content/10_introduction.tex | 4 ++-- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/thesis/bachelor.pdf b/thesis/bachelor.pdf index a4089a731c0da2143bf805959613f6ea97de1552..9758c9124ceeb002fb53aed796bb9395bf9d93a5 100644 GIT binary patch delta 161437 zcmZs?V{~TG5-r-XZM$RJwr$%szD~!+7u&XNTOHfB(@9>RbMB8j-h2OQtXi|iTziaN zd(Wy`m$!=1--_`9Agl?3Sb+XmJQDcs6WZ56b9*qzA}W=JOto|F){2$qrSD$M17;@a zrE6ocfnPfh4~b^Dg2bK`5iK`grGmXR*A;Y+yJ*Yr+~{@M8e6u9kf)9Q=y+BWlw=8_ zHQQ-2G0IJ3#!wjPCr4Go7>;ioCmU<+N7S78+1%}44N`awt*|;lu{!^1=#fQjmP^{H 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"Aggregate, Project + Sum" ] +fn_nice = [ "Scan A", "Scan B", "Aggregate" ] def read_timings_from_csv(fname) -> tuple[list[float], list[str]]: t = {} @@ -66,6 +66,7 @@ def main(data: tuple[list[float], list[str]], fname, unit): kw["arrowprops"].update({"connectionstyle": connectionstyle}) ax.annotate(f"{data[1][i]} - {data[0][i]:2.2f} {unit}", xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y), horizontalalignment=horizontalalignment, **kw) + plt.rcParams.update({'font.size': 18}) fig.savefig(os.path.join(output_path, fname), bbox_inches='tight') From addc420f8347a9be9b3bf1591f0c4e7c1b8c5271 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Constantin=20F=C3=BCrst?= Date: Fri, 9 Feb 2024 17:59:57 +0100 Subject: [PATCH 09/12] increase width of scaling factor plot for better readability --- thesis/content/30_performance.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/thesis/content/30_performance.tex b/thesis/content/30_performance.tex index 8679ba9..47d1f72 100644 --- a/thesis/content/30_performance.tex +++ b/thesis/content/30_performance.tex @@ -149,7 +149,7 @@ For the results of the Brute-Force approach illustrated in Figure \ref{fig:perf- \label{fig:perf-dsa-analysis:average} \end{subfigure} \hspace{5mm} - \begin{subfigure}[t]{0.45\textwidth} + \begin{subfigure}[t]{0.55\textwidth} \centering \includegraphics[width=\textwidth]{images/plot-dsa-throughput-scaling.pdf} \caption{Scaling Factor for different amounts of participating \gls{dsa}. Determined by formula \\ \(\frac{Throughput}{Basline\ Throughput} * \frac{1}{Utilization\ Factor}\) \\ with the baseline being Throughput for 1 \gls{dsa} and the utilization factor representing the factor of the amount of \gls{dsa}s being used over the baseline.} From 282f7c77361acc8f2ea4946d46c970d93981075f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Constantin=20F=C3=BCrst?= Date: Fri, 9 Feb 2024 18:01:13 +0100 Subject: [PATCH 10/12] improve clarity for c5 explanation of why weak wait and access are required, add section labeling to the section containing this paragraph --- thesis/content/50_implementation.tex | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/thesis/content/50_implementation.tex b/thesis/content/50_implementation.tex index 62d8c65..f83548a 100644 --- a/thesis/content/50_implementation.tex +++ b/thesis/content/50_implementation.tex @@ -104,10 +104,11 @@ The chosen solution for this is to not exchange the handlers-pointer with \textt This secondary value allows \(T_2\) to pass the wait, then perform the exchange of handlers itself. \(T_2\) then checks the local copy of the handlers-pointer for validity. The invalid state now includes both \texttt{nullptr} and the secondary invalid pointer chosen. With this, the deadlock is avoided and \(T_2\) will wait for \(T_1\) completing the processing of the handlers. \par \section{Application to \glsentrylong{qdp}} +\label{sec:impl:application} Applying the \texttt{Cache} to \gls{qdp} is a straightforward process. We adapted the benchmarking code developed by Anna Bartuschka and André Berthold \cite{dimes-prefetching}, invoking Cache::Access for both prefetching and cache access. Due to the high amount of smaller submissions, we decided to forego splitting of tasks unto multiple \gls{dsa} and instead distribute the copy tasks per thread in round-robin fashion to all available. This causes less delay due to submission cost which, as shown in Section \ref{subsec:perf:submitmethod}, rises with smaller tasks. The cache location is fixed to \gls{numa:node} 8, the \gls{hbm} accessor of \gls{numa:node} 0 to which the application will be bound and therefore exclusively run on. \par -During the performance analysis of the developed \texttt{Cache}, we discovered that \gls{intel:dml} does not utilize interrupt-based completion signaling (Section \ref{subsubsec:state:completion-signal}), but instead employs busy-waiting on the completion descriptor being updated. Given that this busy waiting incurs CPU cycles, waiting on task completion is deemed impractical, necessitating code modifications, which we elaborate on below. We extended \texttt{CacheData} and Cache to incorporate support for weak waiting. By introducing a flag configurable in Cache, all instances of \texttt{CacheData} created via \texttt{Cache::Access} will check only once whether the \gls{dsa} has completed processing \texttt{Cache} operation, and if not, return without updating the cache-pointer. Consequently, calls to \texttt{CacheData::GetDataLocation} may return \texttt{nullptr} even after waiting, placing the responsibility on the user to access the data through its source location. For applications prioritizing latency, \texttt{Cache::Access} offers the option for weak access. When activated, the function returns only existing instances of \texttt{CacheData}, thereby avoiding work submission to the \gls{dsa} if the address has not been previously cached or was flushed since the last access. \par +During the performance analysis of the developed \texttt{Cache}, we discovered that \gls{intel:dml} does not utilize interrupt-based completion signaling (Section \ref{subsubsec:state:completion-signal}), but instead employs busy-waiting on the completion descriptor being updated. Given that this busy waiting incurs CPU cycles, waiting on task completion is deemed impractical, necessitating code modifications. We extended \texttt{CacheData} and Cache to incorporate support for weak waiting. By introducing a flag configurable in \texttt{Cache}, all instances of \texttt{CacheData} created via \texttt{Cache::Access} will check only once whether the \gls{dsa} has completed processing \texttt{Cache} operation, and if not, return without updating the cache-pointer. Consequently, calls to \texttt{CacheData::GetDataLocation} may return \texttt{nullptr} even after waiting, placing the responsibility on the user to access the data through its source location. For applications prioritizing latency, \texttt{Cache::Access} offers the option for weak access. When activated, the function returns only existing instances of \texttt{CacheData}, thereby avoiding work submission to the \gls{dsa} if the address has not been previously cached or was flushed since the last access. Using these two options, we can avoid work submission and busy waiting where access latency is paramount. \par Additionally, we observed inefficiencies stemming from page fault handling. Task execution time increases when page faults are handled by the \gls{dsa}, leading to cache misses. Consequently, our execution time becomes bound to that of \gls{dram}, as misses prompt a fallback to the data's source location. When page faults are handled by the CPU during allocation, these misses are avoided. However, the execution time of the first data access through the \texttt{Cache} significantly increases due to page fault handling. One potential solution entails bypassing the system's memory management by allocating a large memory block and implementing a custom memory management scheme. As memory allocation is a complex topic, we opted to delegate this responsibility to the user by mandating the provision of new- and free-like functions akin to the policy functions utilized for determining placement and task distribution. Consequently, the benchmark can pre-allocate the required memory blocks, trigger page mapping, and subsequently pass these regions to the \texttt{Cache}. \par From 1e904b495f575ec0bd5b1775d6a923b85e6b0871 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Constantin=20F=C3=BCrst?= Date: Fri, 9 Feb 2024 18:01:40 +0100 Subject: [PATCH 11/12] add plots from timing qdp to the thesis images catalogue --- thesis/images/plot-timing-cacheaccess.pdf | Bin 0 -> 16740 bytes thesis/images/plot-timing-distprefetch.pdf | Bin 0 -> 11208 bytes thesis/images/plot-timing-dram.pdf | Bin 0 -> 10280 bytes thesis/images/plot-timing-hbm.pdf | Bin 0 -> 11042 bytes thesis/images/plot-timing-prefetch.pdf | Bin 0 -> 11545 bytes 5 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 thesis/images/plot-timing-cacheaccess.pdf create mode 100644 thesis/images/plot-timing-distprefetch.pdf create mode 100644 thesis/images/plot-timing-dram.pdf create mode 100644 thesis/images/plot-timing-hbm.pdf create mode 100644 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