Before the company’s new Zen offerings, it’s fair to say that AMD’s last attempt at building a performance desktop processor was not tremendously successful.
The Bulldozer core released in 2011 had a design that can, at best, be described as idiosyncratic. AMD made three bets with Bulldozer: that general purpose workloads would become increasingly multithreaded, that floating point intensive workloads would become increasingly GPU-driven, and that it would be able to aggressively scale clock speeds.
Accordingly, AMD created processors with oodles of simultaneous threads, relatively long pipelines, relatively narrow pipelines, and relatively few floating point resources. The idea was that clock speed and the GPU would make up for the narrowness and lack of floating point capability. AMD hoped all those threads would be working hard.
Each Bulldozer module could run two threads simultaneously, with two independent integer pipelines and one shared floating point pipeline within a module. Desktop processors shipped with two, three, or four modules for four, six, or eight threads in total. Compared to Bulldozer’s predecessor, the K10, each integer pipeline was narrow: two arithmetic logic units (ALUs) and two address generating units (AGUs), instead of three of each in K10. So, too, was the floating point pipeline, with two 128-bit fused multiply-add (FMA) units that could be paired together to perform a single 256-bit AVX FMA instruction. AMD designed the processor with a base clock speed goal of 4.4GHz.
Bulldozer? Bull💩, more like
None of AMD’s gambles paid off. The high-end desktop parts, with their four modules and eight threads, had an abundance of integer threads. But most consumer workloads still can’t be distributed evenly across eight threads. Single-threaded performance continues to matter a great deal. On the other hand, the sharing of the floating point units means that applications stuffed with floating point arithmetic have too few resources to work with. While GPU-based computing is important in certain workloads—such as scientific supercomputing—mainstream applications still require the CPU for floating point number crunching. Bulldozer leaves them short.





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