From 511013c160d89550cfe3cca4db8d2838158a948d Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Thu, 31 Jul 2025 17:56:27 -0500 Subject: [PATCH] spelling updates --- .wordlist.txt | 115 +++++++++++++++++- .../servers-and-cloud-computing/_index.md | 24 ++-- .../how-to-1.md | 2 +- .../how-to-2.md | 6 +- .../java-perf-flamegraph/1_setup.md | 2 +- 5 files changed, 134 insertions(+), 15 deletions(-) diff --git a/.wordlist.txt b/.wordlist.txt index 796f136e22..5afe320b18 100644 --- a/.wordlist.txt +++ b/.wordlist.txt @@ -4474,4 +4474,117 @@ AssetLib PerformanceStudio VkThread precompiled -rollouts \ No newline at end of file +rollouts +Bhusari +DLLAMA +FlameGraph +FlameGraphs +JSP +KBC +MMIO +Paravirtualized +PreserveFramePointer +Servlet +TDISP +VirtIO +WebSocket +agentpath +alarmtimer +aoss +apb +ata +bpf +brendangregg +chipidea +clk +cma +counterintuitive +cpuhp +cros +csd +devfreq +devlink +dma +dpaa +dwc +ecurity +edma +evice +filelock +filemap +flamegraphs +fsl +glink +gpu +hcd +hns +hw +hwmon +icmp +initcall +iomap +iommu +ipi +irq +jbd +jvmti +kmem +ksm +kvm +kyber +libata +libperf +lockd +mdio +memcg +mmc +mtu +musb +napi +ncryption +netfs +netlink +nfs +ntegrity +nterface +oom +optee +pagemap +paravirtualized +percpu +printk +pwm +qcom +qdisc +ras +rcu +regmap +rgerganov’s +rotocol +rpcgss +rpmh +rseq +rtc +sched +scmi +scsi +skb +smbus +smp +spi +spmi +sunrpc +swiotlb +tegra +thp +tlb +udp +ufs +untrusted +uring +virtio +vmalloc +vmscan +workqueue +xdp +xhci diff --git a/content/learning-paths/servers-and-cloud-computing/_index.md b/content/learning-paths/servers-and-cloud-computing/_index.md index 2cf5628cc6..878d7bd782 100644 --- a/content/learning-paths/servers-and-cloud-computing/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/_index.md @@ -8,8 +8,8 @@ key_ip: maintopic: true operatingsystems_filter: - Android: 2 -- Linux: 154 -- macOS: 10 +- Linux: 157 +- macOS: 11 - Windows: 14 pinned_modules: - module: @@ -22,8 +22,8 @@ subjects_filter: - Containers and Virtualization: 29 - Databases: 15 - Libraries: 9 -- ML: 28 -- Performance and Architecture: 60 +- ML: 29 +- Performance and Architecture: 62 - Storage: 1 - Web: 10 subtitle: Optimize cloud native apps on Arm for performance and cost @@ -47,6 +47,8 @@ tools_software_languages_filter: - ASP.NET Core: 2 - Assembly: 4 - assembly: 1 +- Async-profiler: 1 +- AWS: 1 - AWS CDK: 2 - AWS CodeBuild: 1 - AWS EC2: 2 @@ -65,7 +67,7 @@ tools_software_languages_filter: - C++: 8 - C/C++: 2 - Capstone: 1 -- CCA: 6 +- CCA: 7 - Clair: 1 - Clang: 10 - ClickBench: 1 @@ -77,10 +79,11 @@ tools_software_languages_filter: - Daytona: 1 - Demo: 3 - Django: 1 -- Docker: 17 +- Docker: 18 - Envoy: 2 - ExecuTorch: 1 - FAISS: 1 +- FlameGraph: 1 - Flink: 1 - Fortran: 1 - FunASR: 1 @@ -88,7 +91,7 @@ tools_software_languages_filter: - GCC: 22 - gdb: 1 - Geekbench: 1 -- GenAI: 11 +- GenAI: 12 - GitHub: 6 - GitLab: 1 - Glibc: 1 @@ -114,7 +117,7 @@ tools_software_languages_filter: - Linaro Forge: 1 - Litmus7: 1 - Llama.cpp: 1 -- LLM: 9 +- LLM: 10 - llvm-mca: 1 - LSE: 1 - MariaDB: 1 @@ -132,6 +135,7 @@ tools_software_languages_filter: - Ollama: 1 - ONNX Runtime: 1 - OpenBLAS: 1 +- OpenJDK-21: 1 - OpenShift: 1 - OrchardCore: 1 - PAPI: 1 @@ -144,7 +148,7 @@ tools_software_languages_filter: - RAG: 1 - Redis: 3 - Remote.It: 2 -- RME: 6 +- RME: 7 - Runbook: 71 - Rust: 2 - snappy: 1 @@ -161,6 +165,7 @@ tools_software_languages_filter: - TensorFlow: 2 - Terraform: 11 - ThirdAI: 1 +- Tomcat: 1 - Trusted Firmware: 1 - TSan: 1 - TypeScript: 1 @@ -173,6 +178,7 @@ tools_software_languages_filter: - Whisper: 1 - WindowsPerf: 1 - WordPress: 3 +- wrk2: 1 - x265: 1 - zlib: 1 - Zookeeper: 1 diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-1.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-1.md index 5406146e29..6838a42e06 100644 --- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-1.md +++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-1.md @@ -46,7 +46,7 @@ If everything was built correctly, you should see a list of all the available fl Communication between the master node and the worker nodes occurs through a socket created on each worker. This socket listens for incoming data from the master—such as model parameters, tokens, hidden states, and other inference-related information. {{% notice Note %}}The RPC feature in llama.cpp is not secure by default, so you should never expose it to the open internet. To mitigate this risk, ensure that the security groups for all your EC2 instances are properly configured—restricting access to only trusted IPs or internal VPC traffic. This helps prevent unauthorized access to the RPC endpoints.{{% /notice %}} -Use the following command to start the listeneing on the worker nodes: +Use the following command to start the listening on the worker nodes: ```bash bin/rpc-server -p 50052 -H 0.0.0.0 -t 64 ``` diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md index 9a41ca72da..65a020ccab 100644 --- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md +++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md @@ -190,7 +190,7 @@ llama_perf_context_print: eval time = 77429.95 ms / 127 runs ( 609 llama_perf_context_print: total time = 79394.06 ms / 132 tokens llama_perf_context_print: graphs reused = 0 ``` -That's it! You have sucessfully run the llama-3.1-8B model on CPUs with the power of llama.cpp RPC functionality. The following table provides brief description of the metrics from `llama_perf`:

+That's it! You have successfully run the llama-3.1-8B model on CPUs with the power of llama.cpp RPC functionality. The following table provides brief description of the metrics from `llama_perf`:

| Log Line | Description | |-------------------|-----------------------------------------------------------------------------| @@ -200,11 +200,11 @@ That's it! You have sucessfully run the llama-3.1-8B model on CPUs with the powe | eval time | Time to generate output tokens by forward-passing through the model. | | total time | Total time for both prompt processing and token generation (excludes model load). | -Lastly to set up OpenAI compatible API, you can use the `llama-server` functionality. The process of implementing this is described [here](/learning-paths/servers-and-cloud-computing/llama-cpu) under the "Access the chatbot using the OpenAI-compatible API" section. Here is a snippet, for how to set up llama-server for disributed inference: +Lastly to set up OpenAI compatible API, you can use the `llama-server` functionality. The process of implementing this is described [here](/learning-paths/servers-and-cloud-computing/llama-cpu) under the "Access the chatbot using the OpenAI-compatible API" section. Here is a snippet, for how to set up llama-server for distributed inference: ```bash bin/llama-server -m /home/ubuntu/model.gguf --port 8080 --rpc "$worker_ips" -ngl 99 ``` -At the very end of the output to the above command, you will see somethin like the following: +At the very end of the output to the above command, you will see something like the following: ```output main: server is listening on http://127.0.0.1:8080 - starting the main loop srv update_slots: all slots are idle diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md index 771a681efc..6fbe8aeb81 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md @@ -87,7 +87,7 @@ Move the executable to somewhere in your PATH: sudo cp wrk /usr/local/bin ``` -3. Finally, you can run the benchamrk of Tomcat through wrk2. +3. Finally, you can run the benchmark of Tomcat through wrk2. ```bash wrk -c32 -t16 -R50000 -d60 http://${tomcat_ip}:8080/examples/servlets/servlet/HelloWorldExample ```