ACM Transactions on Accessible Computing

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This quarterly publication is a quarterly journal that publishes refereed articles addressing issues of computing as it impacts the lives of people with disabilities. The journal will be of particular interest to SIGACCESS members and delegrates to its affiliated conference (i.e., ASSETS), as well as other international accessibility conferences.

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to whom all this might be most beneficial and opening possible directions for future research.

Rationales

The current era of supercomputing is referred to as the petascale era. The next big HPC challenge is to break the exascale barrier. However, due to technological limitations, 16, 23 there is growing agreement that reaching this goal will require a substantial shift toward hardware/software co-design. 3, 7, 10, 18 The driving idea behind the custom dataflow supercomputers (like the Maxeler solution), falls into this category: To implement the computational dataflow in a custom hardware accelerator. In order to achieve maximum performance, the kernel of the application is compiled into a dataflow engine. The resulting array structure can be hundreds to thousands of pipeline stages deep. Ideally, in the static dataflow form, data can enter and exit each stage of the pipeline in every cycle. It cannot be precisely itemized what portion of the improved performance is due to the dataflow concept, and what portion is due to customization; this is because the dataflow concept is used as a vehicle that provides customization in hardware.

For these dataflow systems, utilization of a relatively slow clock is typical, while the entire dataflow is completed more efficiently. This slow clock is not a problem for big data computations, since the speed of computation depends on pin throughput and local memory size/bandwidth inside the computational chip. Even when the dataflow is implemented using FPGA chips, and thus the general-purpose connections in FPGA chips bring a clock slowdown, this does not affect the performance: pin throughput and local memory size/bandwidth are the bottleneck. The sheer magnitude of the dataflow parallelism can be used to overcome the initial speed disadvantage. Therefore, if counting is oriented to performance measures correlated with clock speed, these systems perform poorly. However, if counting is oriented to performance measures sensitive to the amount of data processed, these systems may perform richly. This is the first issue

the current era of
supercomputing
is referred to as
the petascale era.
the next big hPC
challenge is to break
the exascale barrier.

of importance.

The second important issue is related to the fact that, due to their lower clock speed, systems based on this kind of a dataflow approach consume less power, less space, and less money compared to systems driven by a fast clock. Weston24 shows the measured speedups (31x and 37x) were achieved while reducing the power consumption of a 1U compute node. Combining power and performance measures is a challenge that is already starting to be addressed by the Green 500 list. However, evaluating radically different models of computation such as dataflow remains yet to be addressed, especially in the context of total cost of ownership.

In addition to the aforementioned issues, the third issue of importance is that systems based on a kind of data-flow approach perform poorly on relatively simple benchmarks, which are typically not rich in the amount and variety of data structures. However, they perform fairly well on relatively sophisticated benchmarks, rich in the amount and variety of data structures.

Justification

Performance of an HPC system depends on the adaption of a computational algorithm to the problem, dis-cretization of the problem, mapping onto data structures and representable numbers, the dataset size, and the suitability of the underlying architecture compared to all other choices in the spectrum of design options. In light of all these choices, how does one evaluate a computer system’s suitability for a particular task such as climate modeling or genetic sequencing?