Blog posts exploring the concept "Cache-Hierarchy"
← Back to all tagsBlog posts exploring the concept "Cache-Hierarchy"
← Back to all tagsModern 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.
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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.
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