Understanding the Direct Association Between RAM and CPU in Computer Systems

Central Processing Unit (CPU) and Computer Memory (RAM) are crucial components of computer systems that work in conjunction to process and store data. The CPU is the processing unit that performs data operations, executing instructions to run the operating system and applications. Meanwhile, RAM acts as the storage device for temporarily storing data required by the CPU for immediate access and processing.

Functions of CPU and Computer Memory

The functions between the CPU and computer memory are often implemented at the kernel/silicon level, which would require a detailed explanation to fully understand. However, from an operating system's perspective, there are ways to directly access memory.

Any program can allocate space in the RAM by calling for new variables, using the stack (which automatically manages memory within a function) or the heap (where memory can be manually managed). For instance, in C, one can allocate RAM for a program to interact with.

Accessing RAM with C Programming

include stdlib.h
include stdio.h
int main(int argc, char * argv[]){
    char * string  (char *)malloc(4096); // Allocate 4096 bytes of RAM from the heap and place into a pointer.
    char c[4096]; // Allocate 4096 bytes of stack memory as a char string
    printf(Enter a character: );
    int input  fgetc(stdin);
    free(string); // Release the string from heap after an input is given.
    return 0; // Destroy stack variables
}

Efficiency and Multi-Core Processors

Each processor lacks its own memory, which leads to inefficiency as time is spent waiting for other processors to complete their tasks. Memory processing is typically done via matrices and can be faster when performed across multiple fields. This means that multiple processors often access memory simultaneously, which can be less efficient, especially in multi-core processors with a large number of cores.

The von Neumann architecture is not ideal for parallel programming, as each core would need to access shared memory, which may lead to contention and reduce overall efficiency. In the natural world, the brain is an example of an efficient system where each neuron has its own 'memory' (electrical charge) and does not rely on a common set of wires or buses to share data.

Additionally, evolution has favored parallel processing, where responses are made almost simultaneously. In prehistoric times, only those who could respond in parallel to threats survived, and similarly, when hunting, parallelism was crucial. This highlights the importance of optimizing systems for parallel processing for optimal performance and efficiency.