Concept: Cache-Hierarchy

Modern computing systems present a fundamental paradox: while processor speeds have increased exponentially, memory latency improvements have been modest, creating an ever-widening performance gap. This disparity manifests most acutely in the cache hierarchy, where the difference between an L1 cache hit (approximately 4 cycles) and main memory access (200+ cycles) represents a fifty-fold performance penalty. For systems pursuing native performance without runtime overhead, understanding and exploiting cache behavior becomes not merely an optimization, but an architectural imperative.

As we explored in our companion piece on CPU cache optimization, the Firefly compiler’s Alex component is being designed to perform sophisticated transformations that would align F# code with hardware memory hierarchies. When we consider GPU architectures, we encounter a fundamentally different memory landscape that would require equally different optimization strategies. While GPUs currently dominate parallel computing workloads, we view them as a necessary bridge to more efficient architectures. As discussed in “The Uncomfortable Truth of Comfortable Dysfunction”, the industry’s reliance on GPU architectures represents both a practical reality we must address and an architectural compromise we’re working to transcend.