* [MAQAO] Info: Detected 1 Lprof instances in ip-172-31-18-66.
If this is incorrect, rerun with number-processes-per-node=X
[0mOMP: pid 27227 tid 27294 thread 1 bound to OS proc set {1}
OMP: pid 27227 tid 27295 thread 2 bound to OS proc set {2}
OMP: pid 27227 tid 27296 thread 3 bound to OS proc set {3}
OMP: pid 27227 tid 27227 thread 0 bound to OS proc set {0}
OMP: pid 27227 tid 27297 thread 4 bound to OS proc set {4}
OMP: pid 27227 tid 27298 thread 5 bound to OS proc set {5}
OMP: pid 27227 tid 27299 thread 6 bound to OS proc set {6}
OMP: pid 27227 tid 27302 thread 9 bound to OS proc set {9}
OMP: pid 27227 tid 27300 thread 7 bound to OS proc set {7}
OMP: pid 27227 tid 27301 thread 8 bound to OS proc set {8}
OMP: pid 27227 tid 27305 thread 12 bound to OS proc set {12}
OMP: pid 27227 tid 27303 thread 10 bound to OS proc set {10}
OMP: pid 27227 tid 27304 thread 11 bound to OS proc set {11}
OMP: pid 27227 tid 27306 thread 13 bound to OS proc set {13}
OMP: pid 27227 tid 27307 thread 14 bound to OS proc set {14}
OMP: pid 27227 tid 27308 thread 15 bound to OS proc set {15}
OMP: pid 27227 tid 27310 thread 17 bound to OS proc set {17}
OMP: pid 27227 tid 27309 thread 16 bound to OS proc set {16}
OMP: pid 27227 tid 27326 thread 33 bound to OS proc set {33}
OMP: pid 27227 tid 27342 thread 49 bound to OS proc set {49}
OMP: pid 27227 tid 27328 thread 35 bound to OS proc set {35}
OMP: pid 27227 tid 27327 thread 34 bound to OS proc set {34}
OMP: pid 27227 tid 27313 thread 20 bound to OS proc set {20}
OMP: pid 27227 tid 27314 thread 21 bound to OS proc set {21}
OMP: pid 27227 tid 27343 thread 50 bound to OS proc set {50}
OMP: pid 27227 tid 27315 thread 22 bound to OS proc set {22}
OMP: pid 27227 tid 27317 thread 24 bound to OS proc set {24}
OMP: pid 27227 tid 27312 thread 19 bound to OS proc set {19}
OMP: pid 27227 tid 27319 thread 26 bound to OS proc set {26}
OMP: pid 27227 tid 27330 thread 37 bound to OS proc set {37}
OMP: pid 27227 tid 27321 thread 28 bound to OS proc set {28}
OMP: pid 27227 tid 27333 thread 40 bound to OS proc set {40}
OMP: pid 27227 tid 27344 thread 51 bound to OS proc set {51}
OMP: pid 27227 tid 27325 thread 32 bound to OS proc set {32}
OMP: pid 27227 tid 27323 thread 30 bound to OS proc set {30}
OMP: pid 27227 tid 27322 thread 29 bound to OS proc set {29}
OMP: pid 27227 tid 27316 thread 23 bound to OS proc set {23}
OMP: pid 27227 tid 27320 thread 27 bound to OS proc set {27}
OMP: pid 27227 tid 27338 thread 45 bound to OS proc set {45}
OMP: pid 27227 tid 27341 thread 48 bound to OS proc set {48}
OMP: pid 27227 tid 27335 thread 42 bound to OS proc set {42}
OMP: pid 27227 tid 27334 thread 41 bound to OS proc set {41}
OMP: pid 27227 tid 27340 thread 47 bound to OS proc set {47}
OMP: pid 27227 tid 27329 thread 36 bound to OS proc set {36}
OMP: pid 27227 tid 27324 thread 31 bound to OS proc set {31}
OMP: pid 27227 tid 27336 thread 43 bound to OS proc set {43}
OMP: pid 27227 tid 27318 thread 25 bound to OS proc set {25}
OMP: pid 27227 tid 27346 thread 53 bound to OS proc set {53}
OMP: pid 27227 tid 27311 thread 18 bound to OS proc set {18}
OMP: pid 27227 tid 27331 thread 38 bound to OS proc set {38}
OMP: pid 27227 tid 27350 thread 57 bound to OS proc set {57}
OMP: pid 27227 tid 27353 thread 60 bound to OS proc set {60}
OMP: pid 27227 tid 27351 thread 58 bound to OS proc set {58}
OMP: pid 27227 tid 27337 thread 44 bound to OS proc set {44}
OMP: pid 27227 tid 27339 thread 46 bound to OS proc set {46}
OMP: pid 27227 tid 27354 thread 61 bound to OS proc set {61}
OMP: pid 27227 tid 27348 thread 55 bound to OS proc set {55}
OMP: pid 27227 tid 27347 thread 54 bound to OS proc set {54}
OMP: pid 27227 tid 27345 thread 52 bound to OS proc set {52}
OMP: pid 27227 tid 27332 thread 39 bound to OS proc set {39}
OMP: pid 27227 tid 27349 thread 56 bound to OS proc set {56}
OMP: pid 27227 tid 27355 thread 62 bound to OS proc set {62}
OMP: pid 27227 tid 27352 thread 59 bound to OS proc set {59}
OMP: pid 27227 tid 27356 thread 63 bound to OS proc set {63}
what is a LLM? and why should i care?
A Large Language Model (LLM) is a type of artificial intelligence (AI) that can process and generate human-like text based on the input it receives. LLMs are trained on vast amounts of text data, which allows them to learn patterns, relationships, and context in language. This enables them to generate coherent and often informative responses to user queries.
Here are some reasons why you should care about LLMs:
1. **Improved search and content generation:** LLMs can help improve search results by providing more accurate and relevant information. They can also generate content such as articles, blog posts, and even entire books.
2. **Personalized experiences:** LLMs can be used to create personalized experiences for users. For example, they can generate customized news feeds, product recommendations, or even entire stories based on a user's interests and preferences.
3. **Customer support:** LLMs can be used to provide 24/7 customer support by answering frequently asked questions, helping with simple transactions, and even handling complex issues.
4. **Language learning:** LLMs can help language learners by providing personalized feedback, practicing conversations, and even generating language learning materials.
5. **Content creation:** LLMs can be used to create content such as dialogue, scripts, and even entire stories. This can help writers, filmmakers, and other creators to generate ideas and develop their projects.
Some popular examples of LLMs include:
1. **Chatbots:** Many companies use LLMs to power their chatbots, which can help customers with simple transactions, answer frequently asked questions, and even provide customer support.
2. **Virtual assistants:** LLMs are used in virtual assistants like Siri, Google Assistant, and Alexa to provide information, set reminders, and even control smart home devices.
3. **Language translation:** LLMs are used in language translation tools like Google Translate to provide accurate and context-specific translations.
Overall, LLMs have the potential to revolutionize the way we interact with technology, from simple tasks like search and customer support to more complex tasks like content creation and language learning. As LLMs continue to evolve, we can expect to see even more innovative applications and uses for these powerful tools. [end of text]
Your experiment path is /home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/compilers/armclang_4/oneview_results_1757688282/tools/lprof_npsu_run_0
To display your profiling results:
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# LEVEL | REPORT | COMMAND #
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# Functions | Cluster-wide | maqao lprof -df xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/compilers/armclang_4/oneview_results_1757688282/tools/lprof_npsu_run_0 #
# Functions | Per-node | maqao lprof -df -dn xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/compilers/armclang_4/oneview_results_1757688282/tools/lprof_npsu_run_0 #
# Functions | Per-process | maqao lprof -df -dp xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/compilers/armclang_4/oneview_results_1757688282/tools/lprof_npsu_run_0 #
# Functions | Per-thread | maqao lprof -df -dt xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/compilers/armclang_4/oneview_results_1757688282/tools/lprof_npsu_run_0 #
# Loops | Cluster-wide | maqao lprof -dl xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/compilers/armclang_4/oneview_results_1757688282/tools/lprof_npsu_run_0 #
# Loops | Per-node | maqao lprof -dl -dn xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/compilers/armclang_4/oneview_results_1757688282/tools/lprof_npsu_run_0 #
# Loops | Per-process | maqao lprof -dl -dp xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/compilers/armclang_4/oneview_results_1757688282/tools/lprof_npsu_run_0 #
# Loops | Per-thread | maqao lprof -dl -dt xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/compilers/armclang_4/oneview_results_1757688282/tools/lprof_npsu_run_0 #
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