* [MAQAO] Info: Detected 1 Lprof instances in ip-172-31-47-249.ec2.internal.
If this is incorrect, rerun with number-processes-per-node=X
[0mOMP: pid 28695 tid 28796 thread 3 bound to OS proc set {3}
OMP: pid 28695 tid 28794 thread 1 bound to OS proc set {1}
OMP: pid 28695 tid 28795 thread 2 bound to OS proc set {2}
OMP: pid 28695 tid 28798 thread 5 bound to OS proc set {5}
OMP: pid 28695 tid 28797 thread 4 bound to OS proc set {4}
OMP: pid 28695 tid 28802 thread 9 bound to OS proc set {9}
OMP: pid 28695 tid 28799 thread 6 bound to OS proc set {6}
OMP: pid 28695 tid 28695 thread 0 bound to OS proc set {0}
OMP: pid 28695 tid 28801 thread 8 bound to OS proc set {8}
OMP: pid 28695 tid 28806 thread 13 bound to OS proc set {13}
OMP: pid 28695 tid 28800 thread 7 bound to OS proc set {7}
OMP: pid 28695 tid 28803 thread 10 bound to OS proc set {10}
OMP: pid 28695 tid 28805 thread 12 bound to OS proc set {12}
OMP: pid 28695 tid 28807 thread 14 bound to OS proc set {14}
OMP: pid 28695 tid 28804 thread 11 bound to OS proc set {11}
OMP: pid 28695 tid 28808 thread 15 bound to OS proc set {15}
OMP: pid 28695 tid 28809 thread 16 bound to OS proc set {16}
OMP: pid 28695 tid 28810 thread 17 bound to OS proc set {17}
OMP: pid 28695 tid 28811 thread 18 bound to OS proc set {18}
OMP: pid 28695 tid 28826 thread 33 bound to OS proc set {33}
OMP: pid 28695 tid 28812 thread 19 bound to OS proc set {19}
OMP: pid 28695 tid 28827 thread 34 bound to OS proc set {34}
OMP: pid 28695 tid 28842 thread 49 bound to OS proc set {49}
OMP: pid 28695 tid 28843 thread 50 bound to OS proc set {50}
OMP: pid 28695 tid 28858 thread 65 bound to OS proc set {65}
OMP: pid 28695 tid 28815 thread 22 bound to OS proc set {22}
OMP: pid 28695 tid 28813 thread 20 bound to OS proc set {20}
OMP: pid 28695 tid 28814 thread 21 bound to OS proc set {21}
OMP: pid 28695 tid 28828 thread 35 bound to OS proc set {35}
OMP: pid 28695 tid 28844 thread 51 bound to OS proc set {51}
OMP: pid 28695 tid 28860 thread 67 bound to OS proc set {67}
OMP: pid 28695 tid 28821 thread 28 bound to OS proc set {28}
OMP: pid 28695 tid 28859 thread 66 bound to OS proc set {66}
OMP: pid 28695 tid 28825 thread 32 bound to OS proc set {32}
OMP: pid 28695 tid 28819 thread 26 bound to OS proc set {26}
OMP: pid 28695 tid 28830 thread 37 bound to OS proc set {37}
OMP: pid 28695 tid 28822 thread 29 bound to OS proc set {29}
OMP: pid 28695 tid 28829 thread 36 bound to OS proc set {36}
OMP: pid 28695 tid 28846 thread 53 bound to OS proc set {53}
OMP: pid 28695 tid 28834 thread 41 bound to OS proc set {41}
OMP: pid 28695 tid 28823 thread 30 bound to OS proc set {30}
OMP: pid 28695 tid 28816 thread 23 bound to OS proc set {23}
OMP: pid 28695 tid 28841 thread 48 bound to OS proc set {48}
OMP: pid 28695 tid 28833 thread 40 bound to OS proc set {40}
OMP: pid 28695 tid 28831 thread 38 bound to OS proc set {38}
OMP: pid 28695 tid 28818 thread 25 bound to OS proc set {25}
OMP: pid 28695 tid 28847 thread 54 bound to OS proc set {54}
OMP: pid 28695 tid 28848 thread 55 bound to OS proc set {55}
OMP: pid 28695 tid 28845 thread 52 bound to OS proc set {52}
OMP: pid 28695 tid 28851 thread 58 bound to OS proc set {58}
OMP: pid 28695 tid 28838 thread 45 bound to OS proc set {45}
OMP: pid 28695 tid 28835 thread 42 bound to OS proc set {42}
OMP: pid 28695 tid 28849 thread 56 bound to OS proc set {56}
OMP: pid 28695 tid 28852 thread 59 bound to OS proc set {59}
OMP: pid 28695 tid 28853 thread 60 bound to OS proc set {60}
OMP: pid 28695 tid 28863 thread 70 bound to OS proc set {70}
OMP: pid 28695 tid 28867 thread 74 bound to OS proc set {74}
OMP: pid 28695 tid 28857 thread 64 bound to OS proc set {64}
OMP: pid 28695 tid 28850 thread 57 bound to OS proc set {57}
OMP: pid 28695 tid 28817 thread 24 bound to OS proc set {24}
OMP: pid 28695 tid 28864 thread 71 bound to OS proc set {71}
OMP: pid 28695 tid 28861 thread 68 bound to OS proc set {68}
OMP: pid 28695 tid 28820 thread 27 bound to OS proc set {27}
OMP: pid 28695 tid 28840 thread 47 bound to OS proc set {47}
OMP: pid 28695 tid 28870 thread 77 bound to OS proc set {77}
OMP: pid 28695 tid 28862 thread 69 bound to OS proc set {69}
OMP: pid 28695 tid 28874 thread 81 bound to OS proc set {81}
OMP: pid 28695 tid 28856 thread 63 bound to OS proc set {63}
OMP: pid 28695 tid 28865 thread 72 bound to OS proc set {72}
OMP: pid 28695 tid 28871 thread 78 bound to OS proc set {78}
OMP: pid 28695 tid 28854 thread 61 bound to OS proc set {61}
OMP: pid 28695 tid 28839 thread 46 bound to OS proc set {46}
OMP: pid 28695 tid 28837 thread 44 bound to OS proc set {44}
OMP: pid 28695 tid 28869 thread 76 bound to OS proc set {76}
OMP: pid 28695 tid 28824 thread 31 bound to OS proc set {31}
OMP: pid 28695 tid 28855 thread 62 bound to OS proc set {62}
OMP: pid 28695 tid 28868 thread 75 bound to OS proc set {75}
OMP: pid 28695 tid 28866 thread 73 bound to OS proc set {73}
OMP: pid 28695 tid 28875 thread 82 bound to OS proc set {82}
OMP: pid 28695 tid 28876 thread 83 bound to OS proc set {83}
OMP: pid 28695 tid 28836 thread 43 bound to OS proc set {43}
OMP: pid 28695 tid 28873 thread 80 bound to OS proc set {80}
OMP: pid 28695 tid 28832 thread 39 bound to OS proc set {39}
OMP: pid 28695 tid 28877 thread 84 bound to OS proc set {84}
OMP: pid 28695 tid 28878 thread 85 bound to OS proc set {85}
OMP: pid 28695 tid 28879 thread 86 bound to OS proc set {86}
OMP: pid 28695 tid 28885 thread 92 bound to OS proc set {92}
OMP: pid 28695 tid 28872 thread 79 bound to OS proc set {79}
OMP: pid 28695 tid 28882 thread 89 bound to OS proc set {89}
OMP: pid 28695 tid 28880 thread 87 bound to OS proc set {87}
OMP: pid 28695 tid 28888 thread 95 bound to OS proc set {95}
OMP: pid 28695 tid 28881 thread 88 bound to OS proc set {88}
OMP: pid 28695 tid 28884 thread 91 bound to OS proc set {91}
OMP: pid 28695 tid 28887 thread 94 bound to OS proc set {94}
OMP: pid 28695 tid 28883 thread 90 bound to OS proc set {90}
OMP: pid 28695 tid 28886 thread 93 bound to OS proc set {93}
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 text responses to user queries.
LLMs have become increasingly popular in recent years, and for good reason. They offer a range of benefits that can be useful in various aspects of life, from personal to professional.
Here are some reasons why you should care about LLMs:
1. Improved communication: LLMs can help with writing and communication tasks, such as writing emails, reports, and articles. They can also assist with language translation, summarization, and even conversation.
2. Enhanced productivity: LLMs can automate routine tasks, such as data entry, research, and content creation, freeing up time for more creative and strategic work.
3. Better learning: LLMs can help with language learning, providing interactive and adaptive lessons that cater to individual needs and learning styles.
4. Access to knowledge: LLMs can provide quick answers to complex questions, making it easier to access and learn new information.
5. Personalization: LLMs can be fine-tuned to understand individual preferences, interests, and tone, making it possible to create personalized content and interactions.
6. Accessibility: LLMs can help individuals with disabilities, such as language barriers or cognitive impairments, to access and engage with information and services.
7. Fun and entertainment: LLMs can generate creative content, such as stories, poetry, and even entire scripts, for entertainment purposes.
8. Research and innovation: LLMs can aid in scientific research, data analysis, and hypothesis generation, driving innovation and discovery.
Some notable examples of LLMs include:
1. Chatbots: Virtual assistants that can engage in conversation and answer questions.
2. Language translation tools: Like Google Translate, which can translate text and speech in real-time.
3. Writing assistants: Tools like Grammarly and Language Tool, which can help with grammar, syntax, and style.
4. Research assistants: LLMs that can help with literature reviews, data analysis, and research writing.
5. Content generation tools: Like WordLift, which can generate content based on user input and preferences.
In summary, LLMs are powerful tools that can enhance communication, productivity, learning, and access to
Your experiment path is /home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-47-249.ec2.internal/175-802-9624/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1758029729/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-172-31-47-249.ec2.internal/175-802-9624/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1758029729/tools/lprof_npsu_run_0 #
# Functions | Per-node | maqao lprof -df -dn xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-47-249.ec2.internal/175-802-9624/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1758029729/tools/lprof_npsu_run_0 #
# Functions | Per-process | maqao lprof -df -dp xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-47-249.ec2.internal/175-802-9624/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1758029729/tools/lprof_npsu_run_0 #
# Functions | Per-thread | maqao lprof -df -dt xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-47-249.ec2.internal/175-802-9624/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1758029729/tools/lprof_npsu_run_0 #
# Loops | Cluster-wide | maqao lprof -dl xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-47-249.ec2.internal/175-802-9624/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1758029729/tools/lprof_npsu_run_0 #
# Loops | Per-node | maqao lprof -dl -dn xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-47-249.ec2.internal/175-802-9624/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1758029729/tools/lprof_npsu_run_0 #
# Loops | Per-process | maqao lprof -dl -dp xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-47-249.ec2.internal/175-802-9624/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1758029729/tools/lprof_npsu_run_0 #
# Loops | Per-thread | maqao lprof -dl -dt xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-47-249.ec2.internal/175-802-9624/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1758029729/tools/lprof_npsu_run_0 #
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