* [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 51862 tid 51963 thread 3 bound to OS proc set {3}
OMP: pid 51862 tid 51965 thread 5 bound to OS proc set {5}
OMP: pid 51862 tid 51964 thread 4 bound to OS proc set {4}
OMP: pid 51862 tid 51969 thread 9 bound to OS proc set {9}
OMP: pid 51862 tid 51862 thread 0 bound to OS proc set {0}
OMP: pid 51862 tid 51966 thread 6 bound to OS proc set {6}
OMP: pid 51862 tid 51962 thread 2 bound to OS proc set {2}
OMP: pid 51862 tid 51968 thread 8 bound to OS proc set {8}
OMP: pid 51862 tid 51961 thread 1 bound to OS proc set {1}
OMP: pid 51862 tid 51973 thread 13 bound to OS proc set {13}
OMP: pid 51862 tid 51967 thread 7 bound to OS proc set {7}
OMP: pid 51862 tid 51972 thread 12 bound to OS proc set {12}
OMP: pid 51862 tid 51974 thread 14 bound to OS proc set {14}
OMP: pid 51862 tid 51970 thread 10 bound to OS proc set {10}
OMP: pid 51862 tid 51971 thread 11 bound to OS proc set {11}
OMP: pid 51862 tid 51977 thread 17 bound to OS proc set {17}
OMP: pid 51862 tid 51975 thread 15 bound to OS proc set {15}
OMP: pid 51862 tid 51993 thread 33 bound to OS proc set {33}
OMP: pid 51862 tid 51978 thread 18 bound to OS proc set {18}
OMP: pid 51862 tid 51994 thread 34 bound to OS proc set {34}
OMP: pid 51862 tid 52009 thread 49 bound to OS proc set {49}
OMP: pid 51862 tid 51979 thread 19 bound to OS proc set {19}
OMP: pid 51862 tid 52010 thread 50 bound to OS proc set {50}
OMP: pid 51862 tid 51995 thread 35 bound to OS proc set {35}
OMP: pid 51862 tid 51976 thread 16 bound to OS proc set {16}
OMP: pid 51862 tid 51992 thread 32 bound to OS proc set {32}
OMP: pid 51862 tid 51981 thread 21 bound to OS proc set {21}
OMP: pid 51862 tid 52008 thread 48 bound to OS proc set {48}
OMP: pid 51862 tid 51980 thread 20 bound to OS proc set {20}
OMP: pid 51862 tid 51997 thread 37 bound to OS proc set {37}
OMP: pid 51862 tid 51985 thread 25 bound to OS proc set {25}
OMP: pid 51862 tid 51982 thread 22 bound to OS proc set {22}
OMP: pid 51862 tid 51989 thread 29 bound to OS proc set {29}
OMP: pid 51862 tid 51996 thread 36 bound to OS proc set {36}
OMP: pid 51862 tid 51998 thread 38 bound to OS proc set {38}
OMP: pid 51862 tid 51984 thread 24 bound to OS proc set {24}
OMP: pid 51862 tid 52001 thread 41 bound to OS proc set {41}
OMP: pid 51862 tid 51986 thread 26 bound to OS proc set {26}
OMP: pid 51862 tid 51983 thread 23 bound to OS proc set {23}
OMP: pid 51862 tid 51988 thread 28 bound to OS proc set {28}
OMP: pid 51862 tid 51999 thread 39 bound to OS proc set {39}
OMP: pid 51862 tid 52005 thread 45 bound to OS proc set {45}
OMP: pid 51862 tid 51990 thread 30 bound to OS proc set {30}
OMP: pid 51862 tid 51987 thread 27 bound to OS proc set {27}
OMP: pid 51862 tid 52002 thread 42 bound to OS proc set {42}
OMP: pid 51862 tid 52025 thread 65 bound to OS proc set {65}
OMP: pid 51862 tid 51991 thread 31 bound to OS proc set {31}
OMP: pid 51862 tid 52000 thread 40 bound to OS proc set {40}
OMP: pid 51862 tid 52006 thread 46 bound to OS proc set {46}
OMP: pid 51862 tid 52004 thread 44 bound to OS proc set {44}
OMP: pid 51862 tid 52011 thread 51 bound to OS proc set {51}
OMP: pid 51862 tid 52026 thread 66 bound to OS proc set {66}
OMP: pid 51862 tid 52027 thread 67 bound to OS proc set {67}
OMP: pid 51862 tid 52014 thread 54 bound to OS proc set {54}
OMP: pid 51862 tid 52016 thread 56 bound to OS proc set {56}
OMP: pid 51862 tid 52013 thread 53 bound to OS proc set {53}
OMP: pid 51862 tid 52018 thread 58 bound to OS proc set {58}
OMP: pid 51862 tid 52031 thread 71 bound to OS proc set {71}
OMP: pid 51862 tid 52019 thread 59 bound to OS proc set {59}
OMP: pid 51862 tid 52030 thread 70 bound to OS proc set {70}
OMP: pid 51862 tid 52007 thread 47 bound to OS proc set {47}
OMP: pid 51862 tid 52029 thread 69 bound to OS proc set {69}
OMP: pid 51862 tid 52020 thread 60 bound to OS proc set {60}
OMP: pid 51862 tid 52021 thread 61 bound to OS proc set {61}
OMP: pid 51862 tid 52012 thread 52 bound to OS proc set {52}
OMP: pid 51862 tid 52015 thread 55 bound to OS proc set {55}
OMP: pid 51862 tid 52017 thread 57 bound to OS proc set {57}
OMP: pid 51862 tid 52028 thread 68 bound to OS proc set {68}
OMP: pid 51862 tid 52032 thread 72 bound to OS proc set {72}
OMP: pid 51862 tid 52024 thread 64 bound to OS proc set {64}
OMP: pid 51862 tid 52033 thread 73 bound to OS proc set {73}
OMP: pid 51862 tid 52035 thread 75 bound to OS proc set {75}
OMP: pid 51862 tid 52041 thread 81 bound to OS proc set {81}
OMP: pid 51862 tid 52036 thread 76 bound to OS proc set {76}
OMP: pid 51862 tid 52003 thread 43 bound to OS proc set {43}
OMP: pid 51862 tid 52038 thread 78 bound to OS proc set {78}
OMP: pid 51862 tid 52037 thread 77 bound to OS proc set {77}
OMP: pid 51862 tid 52023 thread 63 bound to OS proc set {63}
OMP: pid 51862 tid 52043 thread 83 bound to OS proc set {83}
OMP: pid 51862 tid 52042 thread 82 bound to OS proc set {82}
OMP: pid 51862 tid 52039 thread 79 bound to OS proc set {79}
OMP: pid 51862 tid 52040 thread 80 bound to OS proc set {80}
OMP: pid 51862 tid 52049 thread 89 bound to OS proc set {89}
OMP: pid 51862 tid 52053 thread 93 bound to OS proc set {93}
OMP: pid 51862 tid 52046 thread 86 bound to OS proc set {86}
OMP: pid 51862 tid 52050 thread 90 bound to OS proc set {90}
OMP: pid 51862 tid 52047 thread 87 bound to OS proc set {87}
OMP: pid 51862 tid 52022 thread 62 bound to OS proc set {62}
OMP: pid 51862 tid 52048 thread 88 bound to OS proc set {88}
OMP: pid 51862 tid 52045 thread 85 bound to OS proc set {85}
OMP: pid 51862 tid 52054 thread 94 bound to OS proc set {94}
OMP: pid 51862 tid 52051 thread 91 bound to OS proc set {91}
OMP: pid 51862 tid 52055 thread 95 bound to OS proc set {95}
OMP: pid 51862 tid 52052 thread 92 bound to OS proc set {92}
OMP: pid 51862 tid 52044 thread 84 bound to OS proc set {84}
OMP: pid 51862 tid 52034 thread 74 bound to OS proc set {74}
what is a LLM? and why it matters
A Large Language Model (LLM) is a type of artificial intelligence (AI) that uses machine learning to generate human-like language. It’s a software program that can understand, analyze, and respond to natural language inputs, such as text or speech.
LLMs are trained on vast amounts of text data, which allows them to learn patterns and relationships between words, phrases, and ideas. This training enables them to generate coherent and contextually relevant text, making them useful for various applications, including:
1. Virtual assistants: LLMs can power virtual assistants like Siri, Google Assistant, and Alexa, enabling them to understand and respond to voice commands.
2. Text summarization: LLMs can summarize long pieces of text into concise, relevant summaries, saving users time and effort.
3. Content generation: LLMs can generate original content, such as articles, product descriptions, or even entire books, based on a given topic or style.
4. Language translation: LLMs can translate text from one language to another, helping to break language barriers and facilitate global communication.
5. Chatbots: LLMs can power chatbots that provide customer support, answer frequently asked questions, and engage users in conversations.
6. Research and analysis: LLMs can aid researchers and analysts by providing insights, identifying patterns, and generating reports based on large datasets.
LLMs matter because they have the potential to revolutionize the way we interact with information and each other. They can:
1. Automate repetitive tasks: LLMs can free up human time and effort by automating tasks such as data entry, content moderation, and language translation.
2. Enhance customer experience: By providing personalized and contextually relevant responses, LLMs can improve customer satisfaction and loyalty.
3. Unlock new forms of creativity: LLMs can generate new ideas, concepts, and content, opening up new possibilities for artistic expression, scientific discovery, and innovation.
4. Enable more effective communication: LLMs can facilitate better understanding and collaboration across languages, cultures, and domains, bridging the gap between people and ideas.
However, LLMs also raise concerns about:
1. Bias and accuracy: LLMs can perpetuate biases and inaccuracies present in the training data, potentially leading to misleading or harmful outputs.
2. Job displacement: As LLMs automate tasks, they may displace human workers, particularly in industries where repetitive or routine tasks are prevalent.
3. Security and control: LLMs can be
Your experiment path is /home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-47-249.ec2.internal/175-768-9528/llama.cpp/run/oneview_runs/compilers/armclang_3/oneview_results_1757690510/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/175-768-9528/llama.cpp/run/oneview_runs/compilers/armclang_3/oneview_results_1757690510/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/175-768-9528/llama.cpp/run/oneview_runs/compilers/armclang_3/oneview_results_1757690510/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/175-768-9528/llama.cpp/run/oneview_runs/compilers/armclang_3/oneview_results_1757690510/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/175-768-9528/llama.cpp/run/oneview_runs/compilers/armclang_3/oneview_results_1757690510/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/175-768-9528/llama.cpp/run/oneview_runs/compilers/armclang_3/oneview_results_1757690510/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/175-768-9528/llama.cpp/run/oneview_runs/compilers/armclang_3/oneview_results_1757690510/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/175-768-9528/llama.cpp/run/oneview_runs/compilers/armclang_3/oneview_results_1757690510/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/175-768-9528/llama.cpp/run/oneview_runs/compilers/armclang_3/oneview_results_1757690510/tools/lprof_npsu_run_0 #
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