When I started studying computer architecture, I knew I was entering a field that quietly shapes the way modern technology works. Behind every phone, server, or smart sensor is a set of design decisions that balance speed, energy, and complexity. What I didn’t realize was just how much of this world I would explore through hands-on experiments with gem5, a powerful simulation tool.
Laying the Foundation
The first step in my journey was simply getting things ready. Installing and building gem5 on my own system wasn’t glamorous, but it grounded me in the practical side of architecture. I learned how to structure my work properly—keeping source code, builds, workloads, and results cleanly separated. That organization became essential later when the number of runs and configurations started to multiply.
Running a simple “hello world” program on the x86 ISA may sound trivial, but it gave me confidence. It was proof that my simulation environment worked, and it reminded me that even the most complex systems begin with small steps.
Memory Hierarchy: Where Performance Meets Reality
As I moved deeper, I studied memory hierarchies—the layers of SRAM, DRAM, Flash, and newer technologies like HBM and NVRAM. Each level carries trade-offs between speed, cost, and capacity. What struck me is how much of modern performance depends not on raw processor speed but on how well we hide or manage memory latency.
Through gem5 experiments, I tested cache parameters: adjusting sizes, associativity, and line sizes. The results were eye-opening. Doubling the cache line size, for example, increased per-miss latency but actually improved execution time overall by better capturing spatial locality. It was a practical lesson in how design trade-offs play out in measurable ways.
Instruction-Level Parallelism (ILP): Squeezing More from a Single Core
Another turning point was exploring Instruction-Level Parallelism (ILP). At first, the idea seems simple: overlap independent instructions so the processor stays busy. But the deeper I went, the more I understood the challenges—data, control, and structural hazards constantly get in the way.
I experimented with pipelining, superscalar configurations, branch prediction, and simultaneous multithreading in gem5. Out-of-order execution and speculation, once just abstract textbook concepts, became real when I saw how they changed throughput and latency. At the same time, I came to appreciate why hardware complexity and power consumption limit how far we can push ILP. The so-called “power wall” isn’t just theory—it shapes the future of processor design.
Connecting to Modern Trends
What excites me most is how these classical concepts connect to emerging directions. Heterogeneous architectures—where CPUs, GPUs, and accelerators cooperate—are the industry’s way of sidestepping ILP’s diminishing returns. Similarly, cache innovations, persistent memory, and interconnect standards like CXL point to a world where efficiency and scalability matter as much as raw speed.
Closing Thoughts
Studying computer architecture has been more than an academic exercise for me. It has been a journey from abstract principles to hands-on experiments, where theory meets measurable results. Running gem5 taught me that architecture is not about chasing “the fastest processor” but about balancing trade-offs: latency versus throughput, performance versus power, simplicity versus complexity.
Every experiment left me with a sense of discovery. Whether it was watching a tiny benchmark expose cache behavior or seeing how branch prediction changes pipeline flow, I realized that architecture is as much about creativity as it is about engineering discipline.
I’m still at the beginning of this road, but one thing is clear: understanding computer architecture gives me a new lens to look at technology, not as a black box but as a carefully tuned system where every choice matters.
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