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Until 2006, the typical power-consumption of a chip remained constant for a given silicon area as the transistor size decreased (this evolution is referred to as Dennard scaling). In other words, energy efficiency was following an exponential law similar to Moore's law. This is no longer true, hence radical changes are needed to further improve power efficiency, which is the limiting factor for large-scale computing. Improving the performance under a limited energy budget must be done by rethinking computing systems at all levels: hardware, software, compilers, and runtimes.
On the hardware side, new architectures such as multi-core processors, Graphics Processing Units (GPUs), many-core and FPGA accelerators are introduced, resulting into complex heterogeneous platforms. In particular, FPGAs are now a credible solution for energy-efficient HPC. An FPGA chip can deliver the same computing power as a GPU for an energy budget 10 times smaller.
A consequence of this diversity and heterogeneity is that a given computation can be implemented in many different ways, with different performance characteristics. An obvious example is changing the degree of parallelism: this allows trading execution time for number of cores used. However, many choices are less obvious: for example, augmenting the degree of parallelism of a memory-bounded application will not improve performance. Most architectures involve a complex memory hierarchy, hence memory access patterns have a considerable impact on performance too. The design-space to be explored to find the best performance is much wider than it used to be with older architectures, and new tools are needed to help the programmer explore it. The problem is even stronger for FPGA accelerators, where programmers are expected to design a circuit for their application! Traditional synthesis tools take as input low-level languages like VHDL and Verilog. As opposed to this, high-level languages and hardware compilers (HLS, High-Level Synthesis, that takes as input a C or C-like language and produces a circuit description) are required.
One of the bottlenecks of performance and energy efficiency is data movement. The operational intensity (ratio computation/communication) must be optimized to avoid memory-bounded performance. Compiler analyses are strongly required to explore the trade-offs (operational intensity vs. local memory size, operational intensity vs. peak performance for reconfigurable circuits).
These issues are considered as one of the main challenges in the Hipeac roadmap which, among others, cites the two major issues:
Applications are moving towards global-scale services, accessible across the world and on all devices. Low power processors, systems, and communications are key to computing at this scale. (Strategic Area 2, Data Center Computing ).
Today data movement uses more power than computation. [...] To adapt to this change, we need to expose data movement in applications and optimize them at runtime and compile time and to investigate communication-optimized algorithms (cross-cutting challenge 1, energy efficiency).
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