Category: C Concepts Hub

ps – ?

The ‘ps’ command in Linux shows information about active processes. Options include displaying processes for the current shell, showing all processes on the system, providing full or long-format listings, showing processes not attached to a terminal, displaying all threads for a process, allowing custom formatting, sorting the output, and more. For further details, type ‘man ps’ on bash.

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GDB COMMANDS

This article by Yashwanth Naidu Tikkisetty provides a comprehensive list of gdb commands for debugging. It covers functions like info, set, watch, stepi, disassemble, and more, essential for efficient debugging in embedded systems. The post also emphasizes the importance of inspecting memory maps and displaying directory paths. For more details, visit the LinkedIn post.

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π‘Ίπ’†π’π’†π’„π’•π’Šπ’—π’† π‘­π’–π’π’„π’•π’Šπ’π’ π‘«π’†π’„π’π’‚π’“π’‚π’•π’Šπ’π’ 𝒐𝒗𝒆𝒓 𝒇𝒖𝒍𝒍 𝒉𝒆𝒂𝒅𝒆𝒓 π’Šπ’π’„π’π’–π’”π’Šπ’π’π’”

In embedded systems, optimizing code size is critical. Including only necessary function declarations can reduce memory usage and improve build times. However, this approach may impact code readability, portability, and error risk. Striking a balance and prioritizing clear documentation is key for successful implementation in resource-limited environments.

Read More π‘Ίπ’†π’π’†π’„π’•π’Šπ’—π’† π‘­π’–π’π’„π’•π’Šπ’π’ π‘«π’†π’„π’π’‚π’“π’‚π’•π’Šπ’π’ 𝒐𝒗𝒆𝒓 𝒇𝒖𝒍𝒍 𝒉𝒆𝒂𝒅𝒆𝒓 π’Šπ’π’„π’π’–π’”π’Šπ’π’π’”

GCC – ?

The gcc commands cover optimizations, profiling, security, and low-level operations, essential for embedded systems and performance-critical applications. Techniques include Link-Time Optimizations, dependency file generation, controlling inline functions, architecture-specific optimizations, and static analysis. These techniques prioritize efficient, secure, and compact code in memory-constrained embedded systems.

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π‘«π’†π’‚π’…π’π’π’„π’Œ π’–π’”π’Šπ’π’ˆ π’‡π’Šπ’‡π’

This post demonstrates a deadlock scenario using two FIFOs and a client-server model. The deadlock occurs when both the server and client wait for each other to send a message, resulting in a standstill. The article also outlines steps to avoid deadlock by following a specific sequence of actions.

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𝑾𝒉𝒂𝒕 π’Šπ’” 𝒂 𝑭𝑰𝑭𝑢?

Named pipes, also known as FIFOs, are essential in inter-process communication. They embody the “First In, First Out” concept, serving as intermediaries between processes. Positioned within the file system, they enable seamless communication and offer persistence and flexibility. Understanding named pipes is key to fully appreciate Linux’s capabilities and empower your programming endeavors.

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π‘¬π’™π’‘π’π’π’“π’Šπ’π’ˆ 𝒕𝒉𝒆 π‘»π’˜π’-π‘Ύπ’‚π’š 𝑺𝒕𝒓𝒆𝒆𝒕𝒔 π’Šπ’ π‘³π’Šπ’π’–π’™: 𝑫𝒖𝒑𝒍𝒆𝒙 π‘ͺπ’π’Žπ’Žπ’–π’π’Šπ’„π’‚π’•π’Šπ’π’ π’˜π’Šπ’•π’‰ π‘·π’Šπ’‘π’†π’” πŸ›£οΈ

Unix Pipes allow one-way communication, but Duplex Communication enables two processes to chat back and forth simultaneously. Using two pipes, data can flow both ways between parent and child processes. By coordinating the reading and writing ends, full duplex communication is achieved, despite not guaranteeing simultaneous data exchange.

Read More π‘¬π’™π’‘π’π’π’“π’Šπ’π’ˆ 𝒕𝒉𝒆 π‘»π’˜π’-π‘Ύπ’‚π’š 𝑺𝒕𝒓𝒆𝒆𝒕𝒔 π’Šπ’ π‘³π’Šπ’π’–π’™: 𝑫𝒖𝒑𝒍𝒆𝒙 π‘ͺπ’π’Žπ’Žπ’–π’π’Šπ’„π’‚π’•π’Šπ’π’ π’˜π’Šπ’•π’‰ π‘·π’Šπ’‘π’†π’” πŸ›£οΈ

~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑽

Polynomial time complexity in algorithms causes tasks to become increasingly complex with each additional input, much like managing a family WhatsApp group. Joining the group is O(n), deciphering messages is O(n^2), and managing disputes is O(n^3). Nested loops and multiple operations on the entire dataset indicate polynomial time complexity, such as O(n^2) or O(n^3).

Read More ~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑽

~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑰𝑰

In a linear time scenario, the time taken to complete a task grows with the size of the input. Like avoiding pesky questions at a big family gathering, each relative must be checked before finding the relative of interest. Similarly, in algorithms, checking each element in a list one by one creates a linear relationship. As datasets grow, efficient algorithms become crucial.

Read More ~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑰𝑰

~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑰

O(logn) indicates logarithmic time complexity, where the algorithm’s efficiency grows as the problem size reduces. For instance, in a sorted list search, you progressively eliminate half the list at each step, akin to finding a word in a dictionary by halving the search space. Various examples illustrate logarithmic time complexity, emphasizing its efficiency for large datasets.

Read More ~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑰